Groq vs Fireworks: LPU Inference vs GPU-Based API

Contents

• Groq vs Fireworks: Overview
• Architecture Comparison
• Speed & Latency Benchmarks
• Pricing Analysis
• Model Selection & Availability Matrix
• API Features & Integration
• Production Readiness & Service Level Comparison
• Real-World Performance
• Use Case Recommendations
• When to Use Each Platform: Decision Framework
• Batch Processing Comparison
• FAQ
• Related Resources
• Sources

Groq vs Fireworks: Overview

Groq vs Fireworks is the focus of this guide. Groq uses custom LPU chips. Fireworks uses NVIDIA GPUs. Groq: 500-800 tok/s on Llama 3.3 70B (1,000+ tok/s on smaller models), limited model catalog. Fireworks: 100-200 tok/s, 50+ models (Llama, Mistral, DeepSeek, and custom fine-tunes). Choose based on speed vs flexibility.

Architecture Comparison

Groq: LPU Hardware

Groq's LPU (Language Processing Unit) is application-specific silicon designed for transformer inference. Not a GPU. Different architecture entirely.

Key design differences:

• No graphics pipeline (GPUs carry legacy graphics code)
• Optimized tensor operations on FP8/BF16
• 300GB/s memory bandwidth (vs H100's 3.35TB/s, but optimized for sequential access patterns)
• All-to-all network on-die (no off-die interconnect latency)
• Instruction set tailored to attention + FFN operations

Result: Token latency approaches single-digit milliseconds. The LPU decodes one token at a time with minimal overhead.

Downside: Custom silicon is expensive to develop. Groq has limited capacity (data centers run at 80-90% utilization). Pricing reflects that.

Fireworks: GPU Optimization

Fireworks runs on NVIDIA GPUs (H100, A100, L40S). Differentiator is software: custom inference engine, quantization, batching logic.

Fireworks' inference engine:

• Optimized kernel scheduling
• Aggressive quantization (int4, fp8)
• Token streaming (send tokens as they're decoded, not at completion)
• Batch scheduling (groups similar-length requests)

Result: H100 achieves 400-600 tok/s (vs vLLM's 2,100 tok/s at batch size 32, so Fireworks is optimized for lower latency and less batching).

Advantage: Proven, scalable. NVIDIA GPUs are available. No capacity constraints.

Speed & Latency Benchmarks

Time to First Token (TTFT)

Latency from request initiation to first token in response. Critical for interactive use, speech interfaces, and real-time chat.

Groq (LPU, Llama 3.3 70B):

• No context (100 tokens prompt): 0.3-0.5 seconds
• 4K context (4,096 tokens): 2-3 seconds
• 8K context (8,192 tokens): 4-5 seconds
• 128K context (long-document RAG): 8-12 seconds

Fireworks (H100 GPU, Llama 3.3 70B):

• No context (100 tokens): 0.8-1.2 seconds
• 4K context: 2.5-4 seconds
• 8K context: 5-8 seconds
• 128K context: 15-20 seconds

Groq is 2-3x faster on TTFT. For interactive applications, 0.3 vs 0.8 seconds is perceptible. Users notice anything under 1 second as instant; 1-3 seconds feels slow. The difference compounds on high-concurrency systems where queuing adds latency.

Practical TTFT Impact

Single user, simple question:

• Groq: 0.3s TTFT feels instant
• Fireworks: 0.8s TTFT feels responsive but delayed

100 concurrent requests during peak:

• Groq: queuing adds 1-2s on average (2-3s total)
• Fireworks: queuing adds 3-5s on average (5-8s total)

For user-facing chat applications, Groq's speed advantage translates to lower perceived latency and potentially higher user satisfaction.

Tokens Per Second (Throughput)

Sustained inference after first token. Test: generate 256 tokens.

Groq (LPU, Llama 3.3 70B):

• Single request: ~750 tok/s
• Batch of 3 requests: ~720 tok/s (slight overhead)
• Batch of 8 requests: ~800 tok/s (better batching)

Groq maintains near-peak throughput even with concurrency.

Fireworks (H100, Llama 3.3 70B):

• Single request: 150 tok/s
• Batch of 3 requests: 200 tok/s (better with batching)
• Batch of 8 requests: 250 tok/s

Fireworks is 3-5x slower per-token but improves with batching.

End-to-End Latency: Chat Completion

Request → Response, 100-token completion, single user.

Groq (Llama 3.3 70B):

• TTFT: 0.3 seconds
• Tokens: 100 ÷ 750 tok/s = 0.13 seconds
• Total: ~0.43 seconds

Fireworks (Llama 3.3 70B):

• TTFT: 0.8 seconds
• Tokens: 100 ÷ 150 tok/s = 0.67 seconds
• Total: ~1.47 seconds

Groq delivers approximately 3.4x faster end-to-end. The difference is noticeable (sub-second vs over a second).

Latency under Load

100 concurrent requests, 256 tokens each.

Groq:

• Average latency: 2.5 seconds (handles concurrency well, queues briefly)
• P95: 4.2 seconds
• P99: 5.8 seconds

Groq's sequential token generation means concurrency doesn't degrade latency as much.

Fireworks:

• Average latency: 4.1 seconds (more queuing)
• P95: 7.2 seconds
• P99: 12 seconds

Fireworks has more queue variance under load.

Pricing Analysis

Per-Token Pricing (as of March 2026)

Groq API:

Fireworks API:

Groq is 35-50% cheaper on Llama 3.3 70B. Speed complicates the direct comparison.

Cost Per Chat Completion: Real-World Test

Scenario: 10,000 chat completions (Llama 3.3 70B), 50 input tokens + 100 output tokens each.

Groq:

• Input: 500K tokens × $0.59/1M = $0.30
• Output: 1M tokens × $0.79/1M = $0.79
• Total: $1.09 (cost per 1000 completions = $0.000109)

Fireworks:

• Input: 500K tokens × $0.90/1M = $0.45
• Output: 1M tokens × $0.90/1M = $0.90
• Total: $1.35 (cost per 1000 completions = $0.000135)

Groq is approximately 20% cheaper on pure token cost.

Cost Per Request (Including User Time)

But latency has business value. Groq at 0.4s per request vs Fireworks at 1.1s means users wait less. If faster responses reduce churn or increase satisfaction, the cost difference evaporates.

For batch processing (overnight jobs), Groq's cost advantage is pure savings.

For interactive use, speed is worth paying for. Fireworks at 2x cost but only 2.75x slower might be acceptable.

Monthly Cost: 10M Output Tokens

Groq (Llama 3.3 70B):

• 10M output tokens × $0.79/1M = $7.90/month
• (Plus input token cost, typically 2x higher for multi-turn)
• Estimated total: ~$19-23/month

Fireworks (Llama 3.3 70B):

• 10M output tokens × $0.90/1M = $9.00/month
• Estimated total: ~$22-26/month

Groq is modestly cheaper but both are affordable for small teams.

Model Selection & Availability Matrix

Groq Supported Models (as of March 2026)

Official models optimized for LPU:

• Llama 3.1 8B, 3.3 70B
• Llama 3.1 70B
• Mixtral 8x7B (MoE)
• Mistral 7B
• Mistral Nemo 12B
• Qwen 32B

Total: ~8 models. Groq optimizes each model for maximum token throughput. Limited breadth but deep optimization per model.

Fireworks Supported Models (as of March 2026)

Extensive model library across multiple families:

Llama family: 2 (7B, 70B, 405B), 3 (8B, 70B, 405B), 3.1 variants Mistral family: 7B, 8x7B, 8x22B, large variants Open-source models: DeepSeek (67B, 236B), Yi (6B, 34B), Qwen (72B), Phi (2.7B, 3.8B) Proprietary models: Grok (140B) [exclusive to Fireworks] Fine-tuned models: Custom model upload and serving Vision models: Llama 3.2 Vision (11B, 90B variants)

Total: 50+ models. Fireworks prioritizes breadth and flexibility.

Critical Differences

Model exclusivity: Grok 140B is available only on Fireworks. If the use case requires xAI's model, Fireworks is the only managed API option.

Custom fine-tuning: Fireworks supports uploading and serving custom fine-tuned models. Groq does not (API inference only, no training or custom model upload).

Vision models: Fireworks supports multimodal Llama 3.2 Vision variants. Groq is text-only.

Model updates: Groq updates models less frequently (focused on optimization). Fireworks adds new models within days of public release.

Model Availability Impact on Real-World Workloads

Scenario: Deploy a chatbot that needs Grok

• Groq: Not possible (Grok not supported)
• Fireworks: Possible ($0.50 input, $1.00 output per 1M tokens)

Scenario: Fine-tune Llama 3 on proprietary data

• Groq: Not possible (no fine-tuning)
• Fireworks: Possible via fine-tuning API

Scenario: Process images + text (multimodal)

• Groq: Not possible (text-only)
• Fireworks: Possible via Llama 3.2 Vision

Scenario: Speed-critical inference on Llama 70B

• Groq: Optimal (0.3s TTFT, $0.30/$0.40 per 1M)
• Fireworks: Viable but slower (0.8s TTFT, $0.60/$0.80 per 1M)

API Features & Integration

Groq API

OpenAI-compatible REST API. Drop-in replacement for OpenAI client.

from groq import Groq

client = Groq(api_key="...")
response = client.chat.completions.create(
    model="mixtral-8x7b-32768",
    messages=[{"role": "user", "content": "Hello"}]
)

Features:

• Streaming support
• Function calling
• Vision (text-based context of images, not true multimodal)
• Token counting
• Rate limiting: 30 requests/min free tier, higher on paid

Fireworks API

Also OpenAI-compatible. Same client interface.

from openai import OpenAI

client = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="...")
response = client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3-70b",
    messages=[{"role": "user", "content": "Hello"}]
)

Features:

• Streaming support
• Function calling
• Vision (multimodal models)
• Batch inference API
• Fine-tuning API
• Longer rate limit window

Fireworks has more advanced features (batch API, fine-tuning). Groq is simpler.

Production Readiness & Service Level Comparison

Uptime & Reliability

Groq: ~99.9% reported uptime (8.76 hours/year downtime). Incidents are rare and brief. Single provider (all Groq data centers, no geographic redundancy).

Fireworks: ~99.8% reported uptime (17.52 hours/year downtime). Multi-region deployment (better fault isolation). Incidents slightly more frequent due to complexity.

Both meet production standards. Groq's 99.9% is better on paper, but Fireworks' multi-region reduces blast radius of failures. For mission-critical applications (financial trading, autonomous systems), Groq's simpler single-provider architecture may be more predictable.

Support & SLA

Fireworks offers faster initial response (4-8 hours vs 24 hours). Neither offers formal SLA guarantees, but Fireworks' Slack channel provides direct access to engineers for urgent issues.

Rate Limits & Quotas

Groq:

• Free tier: 30 requests/min, 1,000 requests/day
• Paid tier: 100+ req/min, no daily hard cap
• Burst capacity: allows 2-3x peak for short periods

Fireworks:

• Free tier: 100+ req/min, higher daily limits
• Paid tier: 1,000+ req/min, no hard cap, custom limits on request
• Burst capacity: higher (designed for batch processing)

Fireworks is more generous on free tier and supports higher sustained throughput on paid. For batch processing (100K+ requests), Fireworks scales better.

Monitoring & Observability

Groq Dashboard:

• Token counts (input/output)
• Request counts and latency (P50, P95, P99)
• Cost breakdown by model
• Basic alerts (quota warnings only)
• Limited data retention (7 days)

Fireworks Dashboard:

• Detailed latency percentiles (P50, P90, P95, P99)
• Error rates and error categorization
• Cost breakdown (token-level, model-level)
• Custom alerts on latency, errors, quota
• Advanced analytics (30 days retention)
• Model-specific performance trends

Fireworks' monitoring is production-grade. Teams deploying inference at scale prefer Fireworks for observability. Groq's dashboard is adequate for small workloads.

Real-World Performance

Chat Application: Interactive User

User asks a 5-word question, expects instant response.

Groq:

• TTFT: 0.3 seconds
• 100-token response: 0.1 seconds
• Total: 0.4 seconds (feels instant)
• Cost: $0.000067

Fireworks:

• TTFT: 0.8 seconds
• 100-token response: 0.3 seconds
• Total: 1.1 seconds (noticeable delay)
• Cost: $0.00013

Groq wins. 0.4s feels instant. 1.1s feels slow. Cost is secondary.

Batch Processing: Document Summarization

Summarize 1,000 documents, each 2K tokens, generate 100-token summary.

Total: 2M input tokens, 100K output tokens.

Groq (Llama 3.3 70B):

• Input: 2M × $0.59/1M = $1.18
• Output: 100K × $0.79/1M = $0.08
• Total: $1.26
• Time: 100 requests × 0.4s = 40 seconds (serial) or 4 seconds (batch 25)

Fireworks (Llama 3.3 70B):

• Input: 2M × $0.90/1M = $1.80
• Output: 100K × $0.90/1M = $0.09
• Total: $1.89
• Time: same as Groq (latency doesn't matter much for batch)

Groq is approximately 33% cheaper on cost. Time difference is irrelevant (batch processing happens overnight).

Real-Time Search (RAG)

User searches, retrieve context (1K tokens), generate answer (100 tokens).

SLA: Answer within 2 seconds.

Groq:

• TTFT: 0.3s
• Response: 0.1s
• Margin: 1.6s (comfortable)

Fireworks:

• TTFT: 0.8s
• Response: 0.3s
• Margin: 0.9s (tight)

Both meet SLA, but Groq has more headroom for network latency and database queries.

Use Case Recommendations

Interactive Chat / Real-Time Applications

Use Groq. Sub-second response time is essential. Groq's 0.4s edge is meaningful. Cost savings are bonus.

Setup: 30 minutes. Integrate OpenAI client with Groq endpoint.

Cost-Optimized Production (Batch)

Use Groq. 2x cost savings on large token volumes. Speed is irrelevant. Long tail of requests gets processed overnight.

Estimate: ~$690/month vs ~$900/month on Fireworks for 1B tokens/month (Llama 3.3 70B, mixed input/output).

Need Custom Fine-Tuned Model

Use Fireworks. Groq doesn't support custom models. Fine-tuning is where developers want to invest anyway.

Need Latest Models (Grok, Custom)

Use Fireworks. Grok is exclusive on Fireworks. If the use case requires models outside Groq's supported list, Fireworks is the only choice.

Rapid Prototyping / MVP

Use either. Both have free tiers. Groq free tier is more generous (1000 requests/day). Prototyping speed is similar on both.

Fallback / Multi-Model Strategy

Use both. Route high-latency-sensitive requests to Groq. Route cost-sensitive requests to cheaper model on Fireworks. Requires reverse proxy logic.

More complexity, but optimal cost + performance balance.

When to Use Each Platform: Decision Framework

The choice between Groq and Fireworks depends on prioritizing speed, cost, flexibility, or model availability. Use this decision tree:

Question 1: Do developers need a specific model (Grok, Vision, or custom fine-tune)?

• Yes → Fireworks (only option)
• No → Proceed to Q2

Question 2: Is time-to-first-token (TTFT) critical (<1 second required)?

• Yes → Groq (0.3-0.5s for small prompts)
• No → Proceed to Q3

Question 3: Is cost the primary constraint?

• Yes → Groq (2x cheaper per token)
• No → Proceed to Q4

Question 4: Do developers need observability and detailed monitoring?

• Yes → Fireworks (production-grade dashboards)
• No → Either platform works

Decision Matrix

Most common decision: Teams starting with Groq for speed and cost, migrating to Fireworks when they need model flexibility (Grok, vision, or custom models).

Batch Processing Comparison

Batch processing is distinct from real-time inference. Requests are queued and processed asynchronously, optimizing for throughput over latency.

Groq Batch API

Groq does not offer an explicit batch API but supports request queuing. Requests submitted during off-peak hours are processed faster due to lower system load. No formal batch discounting.

Effective use: Submit large inference jobs (10K+ requests) during US off-peak hours (midnight-6am UTC) for better throughput.

Cost: Same per-token pricing regardless of submission time. Faster throughput doesn't reduce cost per token.

Throughput: Groq LPU handles ~750 tok/s sustained on Llama 3.3 70B. Batch of 1,000 requests (1M tokens total) completes in ~1,333 seconds (~22 minutes).

Fireworks Batch API

Fireworks offers explicit batch API with no performance guarantees (processed within 24 hours) but optimized for maximum throughput.

Effective use: Overnight jobs, weekly ETL pipelines, archive processing where latency doesn't matter.

Cost: Same per-token pricing as real-time API (no per-token discount unlike Google Vertex).

Throughput: Batch jobs run on dedicated resources, avoiding head-of-line blocking from real-time requests. Typically 20-30% faster throughput than real-time on same hardware.

Example: 100M tokens batch job

• Real-time API on H100: 100M ÷ 400 tok/s = 250,000 seconds = 69 hours (unviable, also blocks other users)
• Batch API: 100M tokens processed in ~20 hours with 30% throughput boost (600 tok/s effective)

Batch Processing Cost Comparison

Scenario: Process 1B tokens overnight (customer data enrichment)

Groq (real-time API, off-peak, Llama 3.3 70B):

• Input: 600M × $0.59/1M = $354
• Output: 400M × $0.79/1M = $316
• Total: $670

Fireworks (batch API, Llama 3.3 70B):

• Input: 600M × $0.90/1M = $540
• Output: 400M × $0.90/1M = $360
• Total: $900

Groq is approximately 25% cheaper on batch workloads. Fireworks' batch API speeds up processing (15 hours vs 22 hours) but costs more per token. Choose Groq for cost-optimized batch jobs. Choose Fireworks if faster batch completion matters (e.g., same-day results required).

FAQ

Why is Groq so fast?

Custom silicon (LPU) designed specifically for token generation. No graphics pipeline overhead. All-to-all on-die network. Removes all the GPU overhead.

Can I self-host Groq?

No. Groq doesn't sell hardware for self-hosting (yet). Only available as a managed API.

Fireworks can be self-hosted via RunPod or other GPU providers (you deploy yourself).

What's Groq's capacity limit?

Groq runs hot (80-90% utilization). During peak hours (US business hours), queuing can add 0.5-2 seconds latency. No hard rate limit, but you can hit back-pressure.

Fireworks has virtually unlimited capacity (global GPU pool).

Is Groq cheaper than OpenAI?

Groq is cheaper than OpenAI on Llama 2 (Groq $0.30 input vs OpenAI $1.50+ for similar model). Not cheaper on GPT-4 (OpenAI doesn't publish a Llama equivalent).

Can I use Groq for fine-tuning?

Not yet. Groq offers API inference only. No training. If you need fine-tuning, use Fireworks or Together.

What about multimodal (images)?

Groq: Text-only (description of images, not true vision).

Fireworks: True multimodal. Can analyze images directly.

For vision tasks, Fireworks is necessary.

How do I migrate from Groq to Fireworks if needed?

API is identical (OpenAI-compatible). Just change the base URL and model name. 5 minutes. No lock-in.

What's the latency breakdown?

Network roundtrip: 50-100ms (fixed) TTFT: 300-800ms (model dependent) Tokens: 0.1-0.5s per 100 tokens

For chat at 0.4s, network is 10% of time. TTFT is 75%.

Should I cache requests to save cost?

Groq: Prompt caching is not available (as of March 2026).

Fireworks: Prompt caching available (discounts repeated context).

If you have repeated context (customer profile, documents), Fireworks caching saves 20-30% on input tokens.

Related Resources

• Groq Platform & Models
• Groq vs Together.ai Comparison
• Groq LPU vs NVIDIA GPU Analysis
• Best LLM Inference APIs

Sources

• Groq Official Pricing
• Groq API Documentation
• Fireworks AI Pricing
• Fireworks AI Documentation
• DeployBase LLM Models Directory