Groq API Pricing and Speed
Groq has made headlines with blazing-fast inference speeds thanks to their custom LPU hardware. But speed comes at a price. If you need both fast inference and cost efficiency at volume, dedicated GPU hosting with vLLM often delivers a better balance. Here is the full comparison.
| Groq Model | Input (per 1M) | Output (per 1M) | Speed (tok/s) |
|---|---|---|---|
| LLaMA 3 8B | $0.05 | $0.08 | ~1,200 |
| LLaMA 3 70B | $0.59 | $0.79 | ~330 |
| Mixtral 8x7B | $0.24 | $0.24 | ~500 |
| Gemma 2 9B | $0.20 | $0.20 | ~800 |
Groq’s single-request latency is exceptional. But the cost per token is higher than running the same models yourself, and rate limits restrict throughput during peak usage. Compare across all providers using our GPU vs API cost comparison tool.
Self-Hosted vLLM Performance
vLLM on NVIDIA GPUs cannot match Groq’s single-stream speed, but with continuous batching it delivers impressive aggregate throughput. For production workloads serving multiple concurrent users, total throughput matters more than single-request latency.
| Model on vLLM | GPU Setup | Monthly Cost | Single Speed | Batched Throughput |
|---|---|---|---|---|
| LLaMA 3 8B | 1x RTX 5090 | $149/mo | ~100 tok/s | ~800 tok/s (8 concurrent) |
| LLaMA 3 70B | 2x RTX 6000 Pro 96 GB | $599/mo | ~45 tok/s | ~300 tok/s (8 concurrent) |
| Mixtral 8x7B | 1x RTX 6000 Pro 96 GB | $299/mo | ~55 tok/s | ~400 tok/s (8 concurrent) |
Check real benchmark numbers on our tokens per second benchmark page. For setup guidance, see our self-host LLM guide.
Cost Comparison at Scale
Using LLaMA 3 70B as the benchmark (Groq’s most popular model), here is the cost comparison:
| Monthly Tokens | Groq API ($0.67/1M blended) | vLLM on 2x RTX 6000 Pro | Savings |
|---|---|---|---|
| 1M | $0.67 | $599 | API wins |
| 100M | $67 | $599 | API wins |
| 500M | $335 | $599 | API wins |
| 1B | $670 | $599 | $71 saved (11%) |
| 2B | $1,340 | $599 | $741 saved (55%) |
| 5B | $3,350 | $599 | $2,751 saved (82%) |
| 10B | $6,700 | $899 (4x RTX 6000 Pro) | $5,801 saved (87%) |
Break-even for LLaMA 3 70B: approximately 894M tokens per month. For the 8B model at $0.06 blended, break-even is higher at ~2.5B tokens/month. Use our LLM Cost Calculator for precise figures.
Speed vs Cost: The Real Tradeoff
Groq’s main selling point is speed. Their LPU hardware delivers 3-10x faster single-stream inference than GPU-based solutions. But there are important nuances:
- Single request: Groq is 3-10x faster. If you need the absolute lowest latency for individual requests, Groq wins.
- Concurrent requests: vLLM with continuous batching handles multiple simultaneous users efficiently. Total throughput is comparable.
- Cost per token: At volume, self-hosted is 55-87% cheaper depending on scale.
- Rate limits: Groq imposes strict rate limits. Self-hosted has no limits.
For a deeper dive into serving frameworks, read our vLLM vs Ollama comparison.
Throughput Analysis: Batched vs Single
The real question is: do you need low latency for individual requests, or high throughput for many concurrent requests?
| Scenario | Groq | vLLM (2x RTX 6000 Pro) | Winner |
|---|---|---|---|
| Single user, interactive chat | 330 tok/s | 45 tok/s | Groq |
| 8 concurrent users | 330 tok/s (rate limited) | 300 tok/s total | Comparable |
| Batch processing 1M docs | Rate limited | Unlimited | vLLM |
| 24/7 production API | $670-$6,700/mo | $599 flat | vLLM |
Groq excels for interactive single-user demos. For production workloads, self-hosted vLLM on dedicated GPU servers delivers better economics. Our TCO analysis covers the full picture including uptime and reliability.
When Groq Wins (and When It Doesn’t)
Groq wins when:
- You need sub-second time-to-first-token for interactive applications
- Monthly volume is under 500M tokens
- You do not need data privacy guarantees
Self-hosted vLLM wins when:
- Monthly volume exceeds 1B tokens
- You need GDPR-compliant data processing
- You want predictable flat-rate costs
- You need to run multiple models or fine-tuned variants
- Rate limits are blocking your production workload
See how Groq stacks up against all providers: GPT-4o comparison, DeepSeek comparison, and the complete API cost guide. Also consider alternatives to cloud GPU platforms like RunPod.
The Optimal Setup
Many teams use a hybrid approach: Groq for latency-critical interactive features and self-hosted vLLM for batch processing, embeddings, and high-volume production inference. This gives you the best of both worlds while keeping costs under control.
Start with our best GPU for inference guide to pick the right hardware, then explore the full cost to run a 70B model for detailed pricing.
Unlimited Inference, Zero Rate Limits
Self-host with vLLM on dedicated GPUs. Save up to 87% versus Groq at scale.
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