Fireworks AI vs Groq: Software on GPUs vs Custom Silicon

Who Wins Per Workload

The hardware split decides most of these. Groq owns the latency floor and stock-model simplicity; Fireworks owns anything that needs flexibility.

Quick Comparison

Groq wins on raw speed and headline price for the models it hosts. Fireworks wins on model count, fine-tuning, and enterprise deployment options.

Hardware: LPU vs GPU

The whole comparison flows from one decision: Groq replaced the GPU, Fireworks optimized it.

Groq's Language Processing Unit is a deterministic dataflow chip. Each clock cycle runs the same operations in the same order, with no cache hierarchy, no prefetch logic, and no speculative execution. It uses about 230MB of on-chip SRAM at roughly 150 TB/s instead of HBM, which removes the memory-bandwidth wall that caps GPU token generation. The cost of that design: a single LPU cannot hold a 70B model, so Groq links hundreds of chips across racks to serve one large model. That is GroqCloud's job to manage, not yours.

Fireworks runs Nvidia H100, H200, and B200 GPUs and squeezes them with a proprietary inference stack rather than off-the-shelf vLLM or TensorRT-LLM. Its FireAttention CUDA kernels, speculative decoding (on by default for latency-sensitive deployments), continuous batching, and hardware-specific quantization (FP8 on Hopper, FP4 on Blackwell) are how it competes on speed without custom silicon.

The practical difference: Groq's determinism means latency does not vary with batch size up to capacity, which is excellent for predictable real-time apps. Fireworks' GPU flexibility means it can host almost any open model and let you fine-tune it, which an LPU pipeline is not built to do.

Speed: Groq Leads Single-Stream

Groq is the fastest provider for the models it hosts. Independent benchmarks consistently put it at the top of single-stream throughput.

Groq publishes the per-model speeds above directly on its pricing page. Fireworks does not publish a fixed tok/s per model because GPU throughput varies with batch size and deployment shape; its claim is mechanism-based: FireAttention serves some models several times faster than stock vLLM, and FireAttention V4 on B200 reports a 3.5x throughput gain over SGLang on H200.

For a chatbot or voice agent where one user waits on one stream, Groq's determinism is hard to beat. For a batch pipeline or a tuned model that Groq does not host, Fireworks' flexibility matters more than headline tok/s.

Pricing: Groq Cheaper Per Token, Fireworks Cheaper at Scale

On the specific models Groq hosts, Groq is cheaper per token. Fireworks wins when you need a model Groq does not run or want to fix cost with dedicated GPUs.

Fireworks also runs per-model serverless tiers for the big open models: DeepSeek V4 Flash at $0.14 in / $0.28 out, MiniMax 2.7 at $0.30 in / $1.20 out, GLM 5.1 at $1.40 in / $4.40 out, and a Fast serving path at roughly double Standard for latency-sensitive cases. Its generic size tiers are $0.10 (under 4B), $0.20 (4 to 16B), and $0.90 (over 16B) per million tokens.

Cost on a Real Workload

Model Selection: Fireworks by a Wide Margin

Fireworks hosts far more models. Groq trades breadth for speed.

Fireworks runs 100+ open models across text, vision, audio, image generation, and embeddings, with named serverless tiers for DeepSeek V4 Pro and Flash, GLM 5.1, Qwen 3.6 Plus, Kimi K2.6, and MiniMax 2.7. Anything not pre-listed can run on a dedicated deployment. It also serves embeddings from $0.008 per million tokens, which Groq does not offer.

Groq curates a smaller set tuned to its LPU: Llama variants, GPT-OSS 20B and 120B, Qwen3 32B, Kimi K2, Llama 4 Scout, plus Whisper Large v3 Turbo for speech at a 228x speed factor and $0.04 per hour of audio. If your model is on Groq, it is fast. If it is not, you are out of luck until Groq adds it.

Fine-Tuning & Customization: Fireworks Only

Fireworks is a training-and-serving platform. Groq is a serving platform.

Fireworks ships full LoRA supervised fine-tuning, DPO, and reinforcement fine-tuning through FireOptimizer and the Build SDK. You can run hundreds of LoRA experiments in parallel, deploy a tuned adapter with one click, and serve LoRA on serverless at no extra cost. FireOptimizer's adaptive speculative execution tailors speculative decoding to your data and reports up to 3x latency improvement. LoRA training runs $0.50 per million tokens up to 16B and $3.00 from 16 to 80B.

Groq's fine-tuning endpoint is in closed beta. In practice you bring open weights and Groq serves them fast; it is not where you train. For structured output, Groq supports strict-mode constrained decoding (guaranteed JSON schema adherence) on select models and JSON object mode elsewhere, plus standard tool use. Fireworks supports JSON mode, function calling, and grammar-constrained output as well.

Dedicated & Enterprise

Fireworks exposes more of the deployment stack to you; Groq keeps it managed.

Fireworks holds SOC 2 Type II, HIPAA, and ISO certifications, and offers HIPAA-eligible deployments for healthcare workloads. Groq is SOC 2 Type II compliant via its Trust Center. For regulated industries that need a signed BAA, Fireworks has the more explicit public posture.

When to Use Fireworks AI

• You need a model Groq does not host. DeepSeek V4, GLM 5.1, Qwen 3.6, MiniMax 2.7, or any of 100+ open models, including vision, image, audio, and embeddings.
• You want to fine-tune. LoRA and reinforcement fine-tuning via FireOptimizer, parallel experiments through the Build SDK, and free LoRA serving on serverless.
• You run steady high volume. Dedicated H100/H200 at $7/hr converts a per-token bill into a fixed rate with fast autoscaling.
• You are in a regulated industry. SOC 2 Type II plus HIPAA plus ISO, with HIPAA-eligible deployments.
• You want quantization control. FP8 on Hopper, FP4 (NVFP4) on Blackwell via FireAttention V4 for latency and cost tuning.

When to Use Groq

• You need the lowest latency. ~394 tok/s on Llama 3.3 70B and ~840 tok/s on Llama 3.1 8B, the fastest of independently benchmarked providers.
• You want predictable response times. Deterministic LPU execution keeps latency flat regardless of batch size, ideal for real-time voice and chat.
• Your model is on the menu. Llama, GPT-OSS, Qwen3, Kimi K2, Llama 4 Scout, and Whisper all run fast and cheap.
• You want the cheapest per-token price. $0.05/$0.08 on Llama 3.1 8B and $0.59/$0.79 on Llama 3.3 70B undercut Fireworks on the shared models.
• You want zero infrastructure. No GPU sizing, no quantization choices, just an OpenAI-compatible endpoint that is fast by default.

Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

Frequently Asked Questions

Is Groq faster than Fireworks AI?

For single-stream token generation, yes. Groq's custom LPU runs Llama 3.3 70B at roughly 394 tok/s and Llama 3.1 8B at roughly 840 tok/s, the fastest of independently benchmarked providers. Fireworks uses Nvidia GPUs with FireAttention CUDA kernels and speculative decoding. It is fast and competitive, but Groq leads on raw per-request latency for the models it hosts.

How do Fireworks AI and Groq compare on price?

As of early 2026, Groq prices Llama 3.3 70B at $0.59 input and $0.79 output per million tokens. Fireworks lists models above 16B at $0.90 per million tokens on serverless. Groq is cheaper on the shared models. Fireworks covers far more models and offers dedicated GPUs from $7/hr for predictable high-volume cost.

Can I fine-tune models on Fireworks AI and Groq?

Fireworks has full LoRA and reinforcement fine-tuning through FireOptimizer and the Build SDK, with one-click deployment of tuned adapters and free LoRA serving on serverless. Groq's fine-tuning endpoint is in closed beta; in practice Groq serves existing open weights rather than training new ones.

Which provider has more open models?

Fireworks, by a wide margin. It hosts 100+ open text, vision, audio, image, and embedding models, plus per-model serverless tiers for DeepSeek, GLM, Qwen, Kimi, and MiniMax. Groq runs a curated set tuned for its LPU and optimized for speed over breadth.

Can I run my own fine-tuned weights on Fireworks AI or Groq?

Only Fireworks. You can upload custom LoRA adapters, fine-tune base weights via FireOptimizer, and deploy your own model on a dedicated GPU. Groq serves a fixed menu of curated open models on its LPU and does not let you bring custom weights, so if you need your own fine-tune in production, Fireworks is the only option of the two.