VRAM capacity determines whether a local language model’s weights, KV cache, and runtime memory fit on the GPU. Memory bandwidth helps determine how quickly the GPU can feed that data during output generation. Because decoding is commonly memory-bound, bandwidth can strongly affect tokens per second—but it cannot guarantee a particular speed on its own.
Capacity is whether the model fits; bandwidth is how fast memory feeds it
A local LLM needs more than room for its model weights. GPU memory may also hold the key-value (KV) cache, activations, input/output tensors, and runtime buffers. NVIDIA identifies weights and the KV cache as major memory consumers, while TensorRT-LLM also accounts for activations and I/O tensors. NVIDIA’s inference overview and TensorRT-LLM’s memory documentation explain these components.
As an Amazon Associate I earn from qualifying purchases.
Bandwidth is the rate at which memory can supply data to the GPU’s compute units. A model can fit in VRAM yet generate slowly if each decoding step has to move substantial data and memory cannot supply it quickly enough. Conversely, high bandwidth does not solve a capacity shortfall: if the workload cannot fit, data may need to be placed elsewhere, changing performance.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Weights and KV cache both count toward the working set
As an illustrative calculation, NVIDIA estimates that the weights of a 7-billion-parameter model loaded at FP16 or BF16 occupy about 14 GB. That is a weights estimate, not a complete VRAM budget. In a separate example, NVIDIA estimates about 2 GB for the KV cache of Llama 2 7B at 16-bit precision, batch size 1, and sequence length 4096. Actual cache requirements vary with model architecture, precision, sequence length, and batch size.
#1 Best Overall
- Chipset: AMD RX 7900 XT
- Memory: 20GB GDDR6
- AMD Triple Fan Cooling Solution
- Boost Clock: Up to 2400 MHz
The KV cache stores information from prior tokens so the model can use it while generating the next token. It grows with context length and the number of simultaneous requests. As a result, a model that fits with a short prompt and one request may require more memory at a longer context or higher concurrency.
Why decoding makes bandwidth matter for tokens per second
Local inference has two different phases. During prefill, the system processes the input prompt and computes its intermediate attention states. This work is highly parallel and is often compute-bound. During decode, the model generates output one token at a time, using the weights and cached state to produce each next token.
Rank #2
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
NVIDIA describes data transfers involving weights, keys, values, and activations as a major source of decode latency compared with raw compute speed. Its July 31, 2026, attention guidance identifies HBM bandwidth as the primary decode bottleneck for the workload it analyzes. This helps explain why faster memory can improve output generation when a workload is bandwidth-limited. It does not establish a universal speed increase for every model or GPU.
Longer contexts can increase KV-cache traffic as well as memory requirements. NVIDIA’s guidance also describes exceptions: speculative decoding can increase decode arithmetic intensity and shift a workload toward being compute-bound, while prefix caching can make prefill for a short new prompt behave more like decode when it reuses a long cached sequence. So decoding is commonly, not invariably, memory-bound.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
Why “tokens per second” can be misleading
A token-rate number is useful only when you know which phase and metric it describes. Prompt processing speed measures prefill; it is not the same as output-generation speed. NVIDIA defines inter-token latency (ITL), also called time per output token, as the average time between consecutive generated tokens; its formula excludes time to first token. NVIDIA defines system tokens per second as aggregate output tokens divided by the elapsed benchmark interval from the first request to the final response. NVIDIA’s metrics documentation describes these definitions.
Per-user generation speed and aggregate system throughput are different quantities. Under concurrent load, adding requests can raise aggregate throughput until GPU compute resources saturate; after that point, throughput can fall. A benchmark showing high total throughput under several simultaneous requests does not necessarily mean one user will see the same generation rate.
Rank #4
- System Compatibility Note: 2.5-slot card, 290x123x51mm, two 8-pin power, recommended 700W PSU. Verify chassis clearance before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- AMD RDNA 4 Architecture: RX 9070 GPU with 56 CUs, 3584 stream processors, 3rd gen RT and 2nd gen AI accelerators – built for 1440p/4K gaming.
- Factory Overclocked Performance: Boost clock up to 2520 MHz, game clock 2070 MHz – delivers smooth, high-framerate gaming out of the box.
- 16GB GDDR6 on 256-Bit Bus: High-speed 20 Gbps memory provides exceptional bandwidth for 4K textures, ray tracing, and demanding workloads.
Align the workload before comparing results
For a meaningful comparison, match or account for the following:
Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteWindows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall- Model and tokenizer: different tokenizers make “tokens” non-equivalent units of text.
- Prompt length and generated output length.
- Concurrency or batch size.
- Precision or quantization.
- Inference runtime and kernel settings.
- The reported metric: prompt throughput, time to first token, per-user ITL, or aggregate system throughput.
When these conditions differ, the results answer different questions. Keep prompt-processing rate, time to first token, per-user generation speed, and total throughput separate when they matter to your use case.
Best Value
- AI Performance: 767 AI TOPS
- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
How to assess a GPU for your local LLM workload
Start with the model, precision, context length, and number of concurrent requests you intend to run. Then assess whether the full working set—not just the weights—fits in available VRAM. After that, compare memory bandwidth and measured performance for the same workload and metric. Power, system compatibility, and cost are also product-specific factors; the sources cited here do not rank consumer graphics cards or establish current prices or consumer token rates.
- Check capacity: account for weights, KV cache at your intended context and concurrency, and runtime memory.
- Check bandwidth: treat it as a key factor for decode speed, not a standalone prediction.
- Check the benchmark: confirm that model, prompt, output, concurrency, precision, runtime, and metric align with your intended use.
- Check memory placement: determine whether the workload stays in GPU memory or relies on data outside it, since that affects performance.
Vendor platform figures should not be mistaken for consumer-card specifications. For example, NVIDIA reports 900 GB/s total CPU–GPU NVLink-C2C bandwidth on GH200 and says that is 7× the bandwidth of standard PCIe Gen5 lanes in traditional x86-based GPU servers. Those are platform-interface figures, not a graphics card’s VRAM bandwidth. NVIDIA also reports up to 2× faster time to first token in its GH200-versus-x86-H100 Llama 3 70B multiturn scenario; that vendor-specific result concerns KV-cache offloading and TTFT, not a general doubling of decode tokens per second. NVIDIA’s GH200 article gives the scenario and figures.
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.
