Best Fast LLM Inference APIs for Open-Weight Models, Ranked by Speed, Latency, and Cost

Groq runs open-weight models on its custom Language Processing Unit (LPU), an SRAM-resident architecture that pre-schedules every operation at compile time and removes the dynamic scheduling overhead of GPU inference. The measured result is sub-100ms TTFT on supported models and sustained throughput of 250-394 tokens per second on Llama 3.3 70B at $0.59 input / $0.79 output per million tokens, the lowest first-party price among the providers in this ranking on the reference model. The trade-offs are catalog and ownership: Groq serves only open-weight models that have been explicitly ported to the LPU (Llama variants, gpt-oss, Kimi K2, Qwen3, DeepSeek R1 Distill), and in December 2025 NVIDIA licensed Groq's LPU technology for roughly $20 billion, with GroqCloud continuing to operate independently.

Cerebras runs inference on its Wafer-Scale Engine, a single chip the size of a dinner plate with roughly 900,000 cores and enough on-chip memory to keep large open-weight models resident without external DRAM transfers. Independently benchmarked throughput on gpt-oss-120B reaches roughly 3,000 tokens per second, and Llama 3.3 70B runs in the 1,600-2,000 TPS range, roughly 10-20x the GPU-based providers on the same models. The trade-offs are catalog size and ecosystem maturity: only models explicitly ported to the WSE are available (currently a handful of Llama variants, Qwen3, gpt-oss, and a small set of others), and on-demand capacity can be constrained when a new model goes viral. Cerebras completed a U.S. IPO in May 2026 (Nasdaq: CBRS) at a roughly $66B day-one market cap, with an OpenAI supply relationship behind it.

Fireworks AI runs open-weight models on optimized GPU infrastructure with a proprietary FireAttention inference engine and adaptive speculative decoding, a runtime technique where a smaller draft model proposes tokens that the target model verifies in parallel. The measured result is the highest throughput for a GPU-based stack on frontier-size open weights: DeepSeek V4 Pro at $1.74 input / $3.48 output per million tokens (about 17% cheaper on input than Together on the same model), Kimi K2.6 at $0.95 / $4.00, and Llama 3.3 70B at $0.90 flat. The trade-off is latency: GPU-class TTFT runs 4-6x higher than the custom-silicon providers, and Fireworks gates monthly spend by tier with a fixed 6,000 RPM ceiling even at the top tier. ARR was reported at roughly $800M in early 2026.

Together AI hosts over 200 open-weight models behind a single OpenAI-compatible API and is the most full-stack of the GPU-based providers in this ranking: serverless per-token inference, dedicated H100 endpoints at $6.49/hr, raw HGX H100 clusters from $3.99/hr reserved, and LoRA fine-tuning across most Llama, Mistral, and Qwen sizes including the 405B flagship. Llama 3.3 70B is $1.04 flat per million tokens (mid-pack against Groq at $0.59/$0.79 and Fireworks at $0.90). CEO Vipul Ved Prakash frames the platform as the 'AI Acceleration Cloud' for teams that need both serverless inference and reserved capacity. ARR was reported at roughly $1B in early 2026.

SambaNova runs inference on its fourth-generation Reconfigurable Dataflow Unit (the SN40L), a tightly coupled three-tier memory architecture designed for large sparse models. Published throughput is 435 tokens per second on MiniMax M2.7 and over 600 tokens per second on gpt-oss-120B, with single-rack deployments at roughly 10 kW versus tens of racks for equivalent GPU throughput. The trade-offs are catalog and ecosystem: SambaNova has fewer ported models than Groq, the developer surface trails Groq and Fireworks on observability and tooling, and the company has had a turbulent year (April 2025 layoffs and a Series E down round at roughly $2.2B from a prior $5.1B mark) before refocusing on inference and sovereign AI.