Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11There is no single, universal LLM “speed” number. Measure the metric that matches your workload: time to first token (TTFT) for startup responsiveness, inter-token latency (ITL) or time per output token (TPOT) for streaming smoothness, end-to-end latency for a complete synchronous answer, and output-token throughput for batch or service capacity. Record the model, tokenizer, precision, hardware, serving engine, workload, concurrency, cache state and measurement boundary with every result.
The procedure below gives reproducible measurements and shows how to report tokens per second without confusing per-request decode speed with aggregate server throughput.
As an Amazon Associate I earn from qualifying purchases.
What “LLM inference speed” actually means
Inference has several observable timings. A model can start slowly but generate tokens quickly, or generate smoothly for one user while its aggregate throughput collapses under concurrent load. Saying only “this model produces 80 tokens per second” hides which of those situations you measured.
| Metric | Definition | Best use | Important qualification |
|---|---|---|---|
| TTFT (time to first token) | Elapsed time from request submission until the first streamed output is received. | Interactive chat responsiveness. | Client-side TTFT includes network and queueing; a server timer may exclude them. |
| ITL (inter-token latency) | Wall-clock gap between consecutive streamed output events. | Streaming smoothness. | It approximates per-token decode delay only when each event contains one token. |
| TPOT (time per output token) | (end-to-end latency - TTFT) / (output tokens - 1) for one request. |
Request-level decode speed after the first token. | It is undefined or unhelpful for a one-token answer and can differ from ITL when events bundle tokens. |
| End-to-end latency | Time from request start to the final output token or event. | Synchronous APIs that wait for completion. | State whether timing ends at the final token, event, or HTTP connection close. |
| Output-token throughput | Total generated output tokens divided by the benchmark interval. | Batch generation and aggregate serving capacity. | It is a system-level rate, not the speed experienced by one request. |
| Total-token throughput | Input plus output tokens divided by the benchmark interval. | Workloads where prompt processing and generation are both billable or capacity-relevant. | Compare only when both systems count input and output tokens the same way. |
| Per-request decode speed | Approximately 1 / TPOT tokens per second after the first token. |
Single-request generation behavior. | Do not present it as aggregate server throughput. |
Metric labels are not interchangeable. The vLLM benchmarking documentation states: Metric terminology is not standardized across benchmarking tools.
Always publish the formula, timing boundary and token-counting convention alongside a metric name.
#1 Best Overall
- [Ultra-Lightweight Ear Buds Designed for Small Ears] Each earbud weighs only 3.7g and features a compact, ergonomic in-ear design made especially for small ears. Secure, low-profile, and comfortable for workouts, all-day wear or overnight sleeping.The ultra-slim profile sits flush in your ear with no hard edges poking or rubbing, so you can even sleep on your side without discomfort.
- [Immersive Stereo Sound with TOZO OrigX Technology] TOZO OrigX tuning delivers clear vocals, balanced mids, and natural stereo sound for music, podcasts, and videos.
- [Long Battery Life for Daily Use] Get up to 7 hours of playtime on a single charge, with up to 32 hours total using the charging case—ideal for workdays, commuting, and extended listening sessions.
- [Bluetooth 5.3 & Stable Connection] Bluetooth 5.3 provides fast pairing, stable wireless performance, and reduced dropouts as you move around home or office.
- [Deep Bass with Clear Vocals] High-performance drivers produce punchy bass while keeping vocals clean and detailed for everyday listening.
TTFT, ITL, TPOT and end-to-end latency
TTFT: startup delay
Start the clock when the client submits the request and stop it when the first streamed output arrives. TTFT includes prompt processing (prefill), queueing, scheduling and, for a client measurement, network transit. A high TTFT feels like an unresponsive chat interface even if subsequent tokens arrive rapidly.
ITL: the gap a streaming user sees
For each pair of consecutive streamed events, subtract the earlier receipt timestamp from the later one. The resulting series is ITL. Report its median and tail percentiles rather than just an average: a few long pauses can make a stream feel jerky while the mean looks acceptable.
TPOT: request-level average after the first token
For a request producing N output tokens, calculate (end-to-end latency - TTFT) / (N - 1). The first token is excluded because its path includes prefill and scheduling effects. A request that takes 1,000 milliseconds to finish, has a 200-millisecond TTFT and emits 81 tokens has TPOT of 10 milliseconds, or approximately 100 tokens per second after the first token.
TPOT is an average over a request. If the server sends several tokens in one event, the event-to-event ITL includes the time for that whole bundle, so ITL and TPOT can diverge. Speculative decoding and other multi-token delivery strategies make the distinction especially important.
End-to-end latency: time to a complete answer
Measure from request submission to the final output token or final event. This is the primary user-facing number for a synchronous endpoint that does not display partial output. Include a clear endpoint rule in the report; closing the connection, receiving a final metadata event and receiving the last token are not necessarily simultaneous.
Why prefill and decode behave differently
Inference has two phases. During prefill, the engine processes the complete input prompt and produces the first token. During decode, it generates the response one token at a time while reusing the accumulated key-value context.
NVIDIA describes prefill as generally compute-bound for reasonable prompt lengths and decode as usually memory-bound. Consequently, a longer prompt often raises TTFT without changing the steady-state token gap in the same way. As the conversation grows, the accumulated context must be handled during decoding; long context can therefore slow later token generation and increase ITL or TPOT.
Measure prompt and output lengths separately. Including a system prompt in the user message count, or silently truncating context at a limit, can invalidate a comparison. A benchmark with short prompts and long answers answers a different question from one with long prompts and short answers.
Rank #2
- 【IP68 Waterproof Multifunctinal Smart Watch】Torjalph fitness activity tracker smart watch is IP68 waterproof technology ,you can wear it when swimming,handwashing,it's rain-proof which means that you perform multiple exercise modes outside without worrying about sweat or rain corroding your fitness smart watch,and this watch equipped with a 230 mAh large capacity battery magnetic charging ,and this sports activity tracker watch can be used 5-7 days If fully charged.
- 【Larger 1.83'' Full Screen Watch & Multi-dial Selection】1.83 inch full HD color touch screen smart watch for men / women provides excellent 240*284 resolution image quality ,This Bluetooth smart watch offers a better touch experience and remarkable immersion and ,more than 200 watch faces for you to choose from App"Da Fit ", you can change the dial style according to your mood to wear keeping the world on your wrist
- 【Accurate Activity Tracker Smart Watch 】sports watch activity tracker uses sweat and dust resistant breathable silicone strap, our Fitness Tracker Smart Watch for Android iOS Phones supports 12 sports modes,walking,running,bicycle,swimming,badminton,basketball,football,rope skipping. This bluetooth calls smart watch for men women is lightweight and stylish, bringing more convenient and relaxing sports experience while also providing more protection.
- 【Your 24 Hour Personal Daily Health Monitor】Fitness watch for women men full touch screen IP67 waterproof bluetooth smartwatch for Android iOS Phones with heart rate blood pressure monitor multifunction Andriod smart watch with steps counter sleep monitor sports fitness watch, monitoring blood oxygen saturation allows you to understand your physical condition in real time to track fitness progress ,to know your health status in time.
- 【More Functions Bluetooth Call Smart Watch】This fitness activity smart watch full screen for iPhone and Android phones includes a bunch of new features including:The bluetooth smart watch can hang up the phone, receive and display WhatsAPP /Text /Facebook /Twitter /Messenger reminder.Support sedentary reminder, alarm clock, stopwatch, timer,weather information,music control, remote camera control compatible with Android 5.0 and above or iOS 9.0 and above smartphones.
Choose the headline number for your workload
| Workload | Lead with | Also report |
|---|---|---|
| Interactive streaming chat | TTFT and ITL or TPOT, preferably p50, p95 and p99. | End-to-end latency, output length and error rate. |
| Synchronous completion | End-to-end latency to the final output. | Output-token throughput, TTFT and output length. |
| Offline or batch generation | Aggregate output-token throughput and requests per second. | Queueing, utilization, prompt/output distributions and tail latency. |
| Capacity planning | Throughput at the intended request rate while meeting the latency SLO. | Peak requests per second, prompt and output lengths, concurrency and rejection rate. |
A large aggregate tokens-per-second figure can coexist with poor first-token responsiveness. Conversely, a low single-request decode rate may still produce excellent aggregate capacity when many requests are batched efficiently.
A reproducible measurement procedure
1. Declare the target metric
Write the question before running the test. For example: “What is client-observed TTFT p95 for a streaming chat request?” or “How many output tokens per second does the service sustain at 32 concurrent requests?” Define whether tokens per second means output tokens only, input plus output, or approximately 1 / TPOT for one request.
2. Freeze the system configuration
Record every variable that can change performance:
- Exact model identifier and revision, including any serving-specific conversion.
- Tokenizer and its version.
- Precision or quantization format.
- GPU or CPU type, count, memory and driver version.
- Serving-engine and runtime versions, context limit and batching settings.
- Sampling parameters such as temperature, top-p, maximum output tokens, stop sequences and random seed.
- Streaming mode, endpoint protocol and number of replicas.
Changing any of these can make a result non-comparable even when the model name appears unchanged.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →3. Define the workload distribution
Specify prompt-token and output-token distributions rather than one convenient example. Include system prompts in input counts, conversation history, stop conditions and any multimodal inputs. Document request rate, concurrency and whether requests arrive at a fixed rate, in bursts or as fast as the server can accept them. Use a fixed seed for synthetic prompts or a versioned custom JSONL dataset.
4. Instrument at the client boundary
Capture timestamps at the point the application sends the request, receives the first streamed event, receives every subsequent event and receives the final event. Also log HTTP errors, timeouts, rejected requests and malformed responses. Client-boundary timing reflects what a user experiences; server-side timing is useful for diagnosing engine behavior but may omit network and queue time.
A minimal event log needs a request identifier, send timestamp, event timestamp, event type and token count in that event. From it, calculate TTFT as first-event time minus send time, ITL as each event gap, and end-to-end latency as final-event time minus send time.
5. Warm up before collecting samples
Discard process startup, model-loading and warm-up requests. Then run enough successful requests for p50, p95 and p99 to stabilize. Keep warm-up traffic separate from measured traffic and state how many requests were discarded. A tiny sample can produce an apparently precise percentile that changes dramatically with one slow request.
6. Control prefix and key-value caches
Prefix-cache hits can make TTFT and throughput look unusually good. For independent comparisons, restart or reset relevant caches between runs when practical. Alternatively, publish separate cache-hit and cache-miss scenarios and identify the proportion of each. Never compare a warm-cache run with a cold-cache run without labeling the difference.
Rank #3
- [Low Noise High Fidelity] New bee guitar cord with 22AWG oxygen-free-copper center conductors provides enhanced signal transfer, the PVC insulation material and the high-density stranded copper double shield provide noise rejection for quiet operation
- [Ideal for Choice] New bee 1/4 straight and right angle metal connectors, ensures a secure connection in any instrument, pedal, or amp jack. L shape great fro on stage guitar stands
- [Wide Compatible] Suitable for mixer, power amplifier, microphone, effector, decoder, equalizer, electoronic organ, electric guitar, electric bass and other electronic equipments. Widely used in the installation of smart home, karaoke speaker, professional stage audio, etc
- [Easy Disassemble] Making incidental repairs quick and easy! Very nice, flexible cable
- [Dont Hesite to Order] 24 hours customer services and professional technology team are standing by
7. Report distributions and accounting
At minimum, report:
- Request count, successful-request count and success rate.
- Benchmark duration and requests per second.
- Input tokens, generated output tokens and the counting method.
- Output-token throughput and, if used, total-token throughput.
- TTFT p50, p95 and p99.
- TPOT p50, p95 and p99.
- ITL p50, p95 and p99 when streaming is enabled.
- End-to-end latency p50, p95 and p99.
Means are useful as supplementary values, but percentile distributions expose queueing and contention that averages hide.
8. Sweep concurrency or request rate
Run a single-user test to show per-request behavior, then repeat at the concurrency or arrival rate your application expects. Increase load until latency violates the service-level objective (SLO), errors appear or throughput stops increasing. Plot throughput against p95 or p99 latency; this reveals the operating point instead of rewarding a saturated system for producing a large but unusable average.
Calculating tokens per second correctly
Per-request decode rate
After calculating TPOT, approximate steady-state per-request decode speed as 1 / TPOT. With TPOT of 0.025 seconds, the rate is about 40 output tokens per second after the first token. This excludes the first-token delay and says nothing about how many other requests share the device.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAggregate output-token throughput
Sum generated output tokens from all successful requests and divide by the measured benchmark interval. If 120,000 output tokens are generated during 600 seconds, output-token throughput is 200 tokens per second. Include only the interval defined for the benchmark; do not divide by wall-clock time that includes model startup unless startup is intentionally part of the workload.
Total-token throughput
Add input and output tokens before dividing by the same interval. State whether cached input tokens are counted as input work, whether rejected requests are excluded and whether the tokenizer’s count is used. Two systems can show different total-token rates while doing the same generation work if their accounting conventions differ.
Requests per second is a separate measure
Requests per second equals successful requests divided by the benchmark interval. It depends heavily on output length. A service completing many short requests can have a higher request rate but a lower token rate than a service completing fewer long answers.
Using a standard benchmark tool
The official vLLM command-line benchmark includes vllm bench serve. Its report includes successful requests, benchmark duration, input and generated tokens, request throughput, output-token throughput, total-token throughput, TTFT, TPOT and ITL. It supports custom JSONL datasets, deterministic synthetic data, saved detailed results and selectable percentiles.
When comparing vLLM output with another framework, match the tool’s formulas and measurement point rather than assuming identical metric names mean identical measurements. Confirm whether timestamps are taken at the client or server, how streamed events are grouped and whether throughput includes input tokens.
Rank #4
- Digital Family Calendar: BSIMB digital calendar wall touch screen puts an end to your chaotic life. Designed for busy families, the digital calendar keeps you organised and in step with your family. Assign different colours and tasks to each family member to make family scheduling easier and more intuitive.
- Easy Setup and Auto-Sync: Plug in, connect to WLAN and auto-syn your electric calendar in minutes. Digital wall calendar compatible with Google calendar, iCloud calendar, Outlook calendar, Cozi calendar and Yahoo calendar. Add events directly on the device or through the ecalendar app. Electronic calendar stays current whether you're at work or traveling—no manual updates needed.
- All-In-One Home Assistant: Smart calendar builds habits and encourage independence with personalised chores. Display daily meal plan to reduce the daily ‘What's for dinner?’ stress. To-do lists keep track of everything you want. Turning chores into valuable achievements with stars and rewards, making life easier and more enjoyable.
- Manage Anytime Anywhere: BSIMB electronic calendar for family stay connected with the powerful ecalendar app. Access and manage your digital wall calendar anytime, anywhere with free features like stars & rewards, photo screensaver, chore charts, meals plans, to-do lists etc. Electronic planner paid features like Magic Import tailor advanced tools for users with special needs.
- Stay in Touch with Loved Ones: Share photos or videos from your phone to electronic wall calendar with app, instantly update your life moment, and keep in touch with your family, especially those who are far away; you can also surprise your smart calendar digital wall by sending him a wish card; it's also great for keeping tech-illiterate elders in touch with the youngsters.
How to make two systems comparable
| Comparison axis | What must match or be disclosed |
|---|---|
| Model | Same model family, exact revision, tokenizer and context limit. |
| Numerical format | Same precision or quantization and equivalent kernel settings. |
| Hardware | GPU or CPU type, count, memory configuration and driver. |
| Serving software | Engine version, batching, parallelism, replicas and scheduler settings. |
| Workload | Prompt/output length distributions, system prompts, stop rules and multimodal inputs. |
| Load | Concurrency, arrival pattern, request rate and warm-up policy. |
| Sampling | Temperature, top-p, maximum output, seed and other decoding parameters. |
| Streaming | Enabled or disabled, endpoint protocol and event token bundling. |
| Cache state | Cold, warm or intentionally mixed prefix-cache conditions. |
| Timing boundary | Client send/receive timestamps or server timers, stated explicitly. |
| Success policy | How timeouts, errors, cancellations and rejected requests affect reported rates. |
| Percentiles | Same percentile definitions and the same set of successful or all requests. |
If one of these axes is unknown, mark the comparison as limited rather than presenting a ranking as definitive.
Interpreting latency under load
At low concurrency, TPOT often approximates the speed of one request running mostly alone. As concurrency rises, requests may queue, share memory bandwidth and experience scheduler delays. TTFT usually exposes queueing first; ITL and TPOT can develop long tails when decode work contends for resources. Throughput may continue rising while p95 latency becomes unacceptable, so report both curves.
For capacity planning, specify the peak requests per second, prompt and output lengths and latency SLO. NVIDIA’s 2024 checklist uses those inputs rather than a single hardware-independent tokens-per-second promise.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Useful reference figures, with their limits
NVIDIA’s 2024 worked example describes 3,500 input words, 500 output words, a three-second first-token limit and 31 peak requests per second. The same material gives an example of 2.4 requests per second per DGX H100 system, 2,606 milliseconds first-token latency and 21.4 milliseconds per generated token. These are scenario-specific figures, not universal model or hardware targets; reproduce the workload and configuration before comparing them with your own measurements.
NVIDIA also cites approximately 90 milliseconds per token for fast human reading and 200 milliseconds per token for average reading. Use those numbers only as intuition about perceived streaming pace, not as an engineering SLO or a claim about model quality.
Common measurement mistakes and fixes
Reporting one average tokens-per-second number
Problem: The number may combine short and long requests, hide queueing and fail to say whether it counts input tokens. Fix: publish output and, if relevant, total-token throughput with request count, workload lengths and p50/p95/p99 latency.
Calling TPOT “server throughput”
Problem: 1 / TPOT describes one request after its first token. Fix: label it per-request decode speed and report aggregate output-token throughput separately.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Timing only the server
Problem: Server timers can omit network transit and client-visible queueing. Fix: instrument the client boundary for user experience, and optionally publish server timings as a separate diagnostic.
Ignoring streamed-event bundling
Problem: One event can contain multiple tokens, making ITL differ from TPOT. Fix: record token counts per event and state whether ITL is event-to-event or token-to-token.
Changing prompt length accidentally
Problem: Missing system prompts, different tokenizers or context truncation alter prefill work. Fix: log exact token counts and tokenizer revision for every run.
Comparing warm and cold caches
Problem: Prefix-cache hits can inflate startup and throughput results. Fix: reset caches or publish explicitly labeled hit and miss cases.
Recommended Free Tools
Stopping after a single-user test
Problem: It says little about a multi-user service. Fix: sweep concurrency or request rate and show the latency-throughput trade-off.
Counting failed requests as successful work
Problem: A high token rate is meaningless if many requests time out or are rejected. Fix: report attempted requests, successful requests, errors and success rate, and explain which requests enter throughput totals.
A reporting template
A useful result can be reproduced from a compact record:
- System: model revision, tokenizer, precision, hardware, driver, engine version, context limit and replica count.
- Decode settings: temperature, top-p, maximum output, stop rules, seed and streaming mode.
- Workload: dataset or seed, input-token distribution, output-token distribution, system prompt and multimodal content.
- Load: concurrency, request-rate pattern, warm-up count, cache state and benchmark duration.
- Measurement: client or server boundary, event timestamp method, token accounting and percentile definition.
- Results: request count, success rate, requests per second, output and total tokens per second, TTFT p50/p95/p99, TPOT p50/p95/p99, ITL p50/p95/p99 and end-to-end latency p50/p95/p99.
This format lets a reader distinguish startup delay, steady streaming, complete-response latency and aggregate capacity instead of treating them as one vague speed score.
Free tools Windows power users keep installed
One-click scans. No signup required.
Bottom line
Measure the experience your application delivers. Capture TTFT, every streamed-event timestamp, final completion time and token counts at the client boundary; calculate TPOT and ITL with their exact definitions; then repeat across controlled concurrency and cache conditions. Report distributions and the full configuration. Only then does a tokens-per-second figure become a meaningful, comparable measurement.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
