There is no universally fastest or cheapest API for open-weight LLMs. The answer changes with the model, prompt and response lengths, concurrency, region, service tier, and the latency metric you care about. For a fair choice, compare the same model and request pattern across providers—and weigh serverless per-token billing against the hourly cost and utilization of a dedicated deployment.
What to compare before choosing an LLM inference platform
A provider-wide ranking can mislead: different services may be serving different models, versions, or workloads. Compare one exact model and version at a time, and record both cost and performance for requests shaped like your production traffic.
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- Latency: Measure time to first token separately from generation speed in tokens per second. A single latency number does not describe both.
- Cost: For token APIs, calculate input and output charges separately using your actual token mix. For dedicated deployments, include hourly compute, replica size, and expected utilization.
- Model fit: Confirm the exact model and version, context limit, and required features such as tool calling, structured output, or vision.
- Traffic and operations: Check rate and concurrency limits, scaling behavior, cold starts, regional availability, reliability commitments, account requirements, and data-handling terms directly with the provider.
Hugging Face’s supported-model comparison exposes model/provider-specific input and output prices, context, latency, throughput, and feature fields. These listings are a useful shortlist, not a controlled cross-provider benchmark: the table does not establish that entries were measured under the same conditions. Check the live supported models comparison and Inference Providers documentation for availability and integration details.
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How the listed prices and speed can differ for one model
The following are entries recorded in a Hugging Face comparison-table snapshot for gpt-oss-120b. The snapshot’s capture date is not stated, and the listed latency and throughput are not independently reproduced measurements here. Treat these values as examples of model/provider differences—not as current quotes or a universal ranking—and verify the live table before budgeting.
#1 Best Overall
- EVOLUTION AMD RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
| Provider entry for gpt-oss-120b | Input price per million tokens | Output price per million tokens | Listed latency | Listed throughput |
|---|---|---|---|---|
| Cerebras | $0.35 | $0.75 | 0.15 seconds | 1,296 tokens/second |
| DeepInfra | $0.04 | $0.17 | 0.56 seconds | 61 tokens/second |
| Groq | $0.15 | $0.75 | 0.34 seconds | 431 tokens/second |
All figures in the table are Hugging Face listing values, not a matched test of identical prompts, output lengths, region, concurrency, or account tier. The table’s latency field is not identified here as time to first token, so do not interpret it as such. Consult the live comparison table for current entries and their feature fields.
Serverless token API or dedicated endpoint?
The central decision is whether you want to pay for usage or reserve and operate deployment capacity. Serverless can suit variable demand; a dedicated endpoint can make sense when you need a specific deployment and can keep its capacity usefully occupied. Neither model is automatically cheaper: compare total cost against the tokens actually served and the operational controls you need.
Rank #2
- Built for Local AI Development: AMD Ryzen AI Halo is designed for local AI development and inference, featuring 128GB unified memory and support for up to 200B parameter models to build and run intensive AI workloads locally.
- 128GB Unified Memory: Features 128GB LPDDR5x unified memory at 8000 MT/s with 256 GB/s memory bandwidth, providing a shared memory pool across the CPU, GPU, and NPU to support larger AI models.
- AMD Ryzen AI Max+ 395 Processor: Features 16 cores, 32 threads, and Zen 5 architecture, paired with AMD Radeon 8060S integrated graphics featuring 40 RDNA 3.5 compute units and an AMD XDNA 2 NPU with up to 50 TOPS.
- Linux AI Developer Platform: Purpose-built for Linux-based AI development with full AMD ROCm software support and preloaded tools, models, and workflows optimized for local AI development.
- Compact, Connected Design: Includes a 2TB M.2 SSD, 10GbE LAN, Wi-Fi 7, Bluetooth 5.4, USB-C connectivity, and HDMI 2.1b.
| Consideration | Serverless token API | Dedicated endpoint |
|---|---|---|
| Typical billing basis | Input and output tokens used | Compute billed by time, with configuration and utilization affecting effective cost |
| Demand pattern to evaluate | Variable or bursty traffic; pay-as-you-go may align with usage | Steadier demand or a need for a separately deployed model; account for idle capacity |
| Capacity planning | Check provider limits, scaling behavior, and cold-start characteristics | Choose replica and compute size, then estimate how much capacity your workload will use |
| What to verify | Exact model, rate limits, account/key route, region, and current token prices | Exact hardware configuration, hourly rate, scaling controls, availability, and total cost |
When a token API is a practical starting point
Hugging Face Inference Providers offers access to more than 200 models through multiple providers with centralized pay-as-you-go billing; its pricing documentation says Hugging Face adds no markup. Billing and account requirements differ depending on whether requests are routed through Hugging Face or use a custom provider key, so check the pricing and billing documentation before selecting a route.
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Together AI describes its serverless offer as: “Call any leading open-weight model through a single API, with nothing to deploy or manage, and pay only for the tokens you use.” That is the provider’s product description, not an independent performance finding. See Together AI serverless inference for its current offer and terms.
Rank #3
- EVOLUTION RYZEN AI MAX+ 395 MINI PC - GMKtec EVO-X2 is the next evolution in AI mini PC Ryzen Strix Halo series. Thanks to AMD Simultaneous Multithreading (SMT) the core-count is effectively doubled, to 32 threads. Ryzen AI Max+ 395 has 64 MB of L3 cache and can boost up to 5.1 GHz, depending on the workload. The Ryzen AI Max+ 395 is currently rated as the "most powerful x86 APU" on the market for AI computing.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
When a dedicated endpoint may fit better
Hugging Face Inference Endpoints is a separate product for deploying selected models. Its catalog shows example hourly compute prices for CPU and GPU configurations; those catalog examples are not a total-cost estimate for a particular workload. Estimate capacity needs and utilization before comparing an endpoint with per-token billing. See the Inference Endpoints catalog.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to make a fair speed and cost comparison
- Fix the model and version. Compare the same model with the same version and required features; availability can differ by provider.
- Use representative requests. Include realistic prompt lengths, expected output lengths, and the concurrency your application needs to handle.
- Measure distinct latency outcomes. Record time to first token and generation throughput separately, and state the region, service tier, and test conditions. Repeat tests rather than relying on a single request.
- Calculate usage cost from your token mix. Apply input and output rates separately to expected monthly volumes. For dedicated capacity, compare hourly compute cost with the volume actually served, including periods of lower utilization.
- Check production requirements. Confirm context limits, tools or structured-output support, modalities, rate and concurrency limits, scaling behavior, reliability commitments, and data terms with the provider.
- Choose by workload, not one leaderboard number. Re-test after changing the model, traffic pattern, region, or provider configuration.
The reviewed material does not establish comparable service-level guarantees, regional latency, data handling, or support terms across these platforms. Those terms need direct confirmation for the specific account and deployment you plan to use.
How to shortlist providers without declaring a universal winner
Use Hugging Face’s comparison table to find candidate model/provider combinations and required feature support, then validate pricing and service terms on the provider’s own pages. Cerebras publishes an official Inference pricing page; it is one option to test, but the available figures do not establish a general cost or speed advantage across models and workloads. Together AI’s serverless API is another candidate when token-based use and a single API are relevant. Hugging Face separates its provider-routed token API from its dedicated endpoint product, so choose the product mode that matches your deployment and billing needs.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallIn practice, the cheapest option is the one with the lowest total cost for your actual input/output mix and utilization; the fastest is the one that meets your application’s first-token and generation-speed requirements under its real load. A table snapshot can narrow the candidates, but only a same-model, same-workload comparison can answer those questions for your service.
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