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Groq vs Cerebras: Pricing, Speed, and Benchmark Comparison

Deploybase · April 25, 2025 · Model Comparison

Contents

Groq and Cerebras: Different Approaches to LLM Speed

Groq vs Cerebras represents fundamentally different hardware approaches to LLM inference acceleration. Groq uses specialized LPU (Language Processing Unit) architecture optimizing tensor operations. Cerebras uses massive wafer-scale AI processors (CS-3) with 900K cores on single chips.

Groq's approach:

  • Specialized for sequential token generation
  • Sub-100ms latency targets
  • Lower total throughput than GPU alternatives
  • Optimized for real-time interactive applications

Cerebras's approach:

  • Specialized for large batch processing
  • Excellent total throughput
  • Higher per-token latency than Groq
  • Optimized for volume applications

Both differ from traditional GPU inference (H100, A100). Neither perfectly replicates GPU versatility. Architecture choice depends on workload characteristics.

Groq Hardware Architecture

LPUs (Language Processing Units) contain specialized execution units for tensor operations. Each LPU contains:

  • 600 GB/s bandwidth between compute and memory
  • Sequential token generation optimized
  • Low power consumption compared to GPUs

Groq systems availability:

  • Cloud API access (groq.com)
  • On-premise deployment via partnerships
  • Growing partner network (OVHcloud, Lambda)

Groq's public cloud API offers:

  • Llama 3.3 70B: ~$0.59 per 1M input tokens, ~$0.79 per 1M output tokens
  • Llama 3.1 8B Instant: $0.05 per 1M input tokens, $0.08 per 1M output tokens
  • Llama 4 Scout: $0.11 per 1M input tokens, $0.34 per 1M output tokens

See Groq API pricing for current rates.

Cerebras Hardware Architecture

CS-3 (Cerebras System 3) wafer-scale processors contain:

  • 900,000 AI cores
  • 40GB of on-chip SRAM
  • 120 petaflops peak performance
  • 5000x reduction in latency variance

Cerebras offers a public API with published pricing. Current rates: Llama 3.1 8B at $0.10/M tokens (input and output), GPT OSS 120B at $0.35/$0.75, Qwen 3 235B at $0.60/$1.20, GLM 4.7 at $2.25/$2.75. See Cerebras official pricing for the current model list.

Performance Benchmarking: Latency

Groq's strength manifests as ultra-low latency. Time-to-first-token (TTFT) measures delay from request to first response token.

Groq Llama 3.1 70B:

  • Time-to-first-token: 400-600ms
  • Sustained throughput: 200-300 tokens/second per request
  • Per-token latency: 3-5ms

Cerebras CS-3:

  • Time-to-first-token: 1200-1800ms
  • Sustained throughput: 400-600 tokens/second per request
  • Per-token latency: 1.5-2.5ms

GPU baselines (H100):

  • Time-to-first-token: 2000-4000ms
  • Sustained throughput: 150-250 tokens/second per request
  • Per-token latency: 4-6ms

Groq's latency advantage appears only in TTFT metric. Sustained throughput differences narrow significantly.

Interactive applications (chatbot, real-time coding agents): Groq's sub-1-second TTFT provides significant user experience advantage. Users perceive immediate response vs 1-2 second delay from GPU alternatives.

Batch processing: Throughput metric dominates. Cerebras batch performance exceeds Groq.

Throughput Comparison

Groq throughput (tokens/second across all active requests):

  • Single request: 200-300 tokens/second
  • 4 concurrent requests: 600-800 tokens/second
  • 16 concurrent requests: 1200-1500 tokens/second

Cerebras throughput:

  • Single request: 400-600 tokens/second
  • 4 concurrent requests: 1200-1800 tokens/second
  • 16 concurrent requests: 2400-3600 tokens/second

For high-concurrency services, Cerebras throughput advantage becomes substantial.

Calculate breakeven point: At approximately 6 concurrent requests, Cerebras total throughput exceeds Groq. Services handling 100+ requests/second should favor Cerebras.

Pricing Model Comparison

Groq Cloud API (as of March 2026):

Llama 3.1 70B:

  • Input tokens: $0.59 per 1M
  • Output tokens: $0.79 per 1M
  • No prepaid discounts

Example: 1000 requests, 300 token input, 500 token output

  • Input: 300K tokens × $0.59 = $0.177
  • Output: 500K tokens × $0.79 = $0.395
  • Cost per request: $0.000572

See Groq API pricing.

Cerebras Production (estimated):

Requires minimum commitment. Typical arrangements:

  • 1 year commitment: $50K-100K base
  • Includes 100-500B tokens monthly
  • Overage: $0.10-0.30 per 1M tokens

At 500B monthly tokens (high volume):

  • Base commitment: $75K
  • Monthly cost: $6,250
  • Per-token cost: $0.0125 per 1M tokens (75% cheaper than Groq)

Cerebras advantages appear only at high volume. Break-even approaches 100B tokens monthly (~$30K total at Groq pricing).

Performance Per Dollar

Calculate cost-efficiency across different workloads.

Real-time Chatbot (100 concurrent users, 5-second average interaction)

Groq assumptions:

  • 100 concurrent users
  • Average 3000 tokens output per conversation
  • 300K concurrent tokens in flight
  • Cost per conversation: $0.002
  • 43,200 conversations daily
  • Daily cost: $86.40

Cerebras assumptions (hypothetical, estimated pricing):

  • Same workload
  • Cost per conversation: $0.001 (estimated)
  • 43,200 conversations daily
  • Daily cost: $43.20

Groq provides faster user experience. Cerebras provides lower cost. Hybrid could route to Groq, fallback to Cerebras if queue depth exceeds threshold.

Batch Processing (1B tokens daily in non-interactive batches)

Groq:

  • 1M input tokens average: $0.59
  • 1M output tokens average: $0.79
  • Daily: $1,380
  • Annual: $503,700

Cerebras:

  • Annual commitment: $75K
  • Includes 1.5B tokens monthly
  • Daily cost: $2,500
  • Exceeds committed capacity, cost jumps

Cerebras becomes cost-optimal at 1.5B+ token volume. Below that threshold, Groq's per-token pricing wins.

Benchmarks: Quality and Accuracy

Both providers serve identical model: Llama 3.1 70B.

Quality comparison reduces to inference implementation differences. Both providers' implementations achieve comparable quality. Minimal accuracy difference detected across standard benchmarks.

Latency-induced quality differences don't exist (model quality independent of serving hardware). The same weights produce identical outputs.

Choose based on performance needs, not quality variations.

Use Case Recommendations

Choose Groq for:

  • Real-time applications requiring <1 second TTFT
  • Chatbots and interactive agents
  • Streaming applications (low-latency first-token critical)
  • <100 concurrent requests
  • Cost-conscious single-request services

Example: Customer support chatbot serving 10K daily conversations. Groq's latency improves user experience meaningfully.

Choose Cerebras for:

  • Batch processing workloads
  • High-volume applications (1B+ tokens daily)
  • Multi-GPU equivalent throughput
  • Production deployments with minimum commitments
  • 100+ concurrent requests

Example: Content moderation processing 5M user comments daily. Cerebras's throughput justifies minimum commitment.

Choose GPU (H100) for:

  • Balanced latency and throughput requirements
  • Flexibility (fine-tuning, training, inference)
  • Lower minimum commitments
  • Multi-model serving

Example: Production system needing inference + fine-tuning. GPUs provide broader capabilities.

Hybrid Deployment Strategy

Production systems handling diverse workload patterns should use both:

Route to Groq:

  • Interactive user-facing requests
  • Requests completing within 10 seconds
  • Low-volume specialized tasks

Route to Cerebras:

  • Batch processing
  • High-concurrency scenarios
  • Cost-optimized workloads

Route to H100 GPUs:

  • Model fine-tuning
  • Complex multi-stage pipelines
  • Custom model deployment

Intelligent routing based on estimated processing time:

  • <2 seconds estimate: Groq (latency priority)
  • 2-60 seconds estimate: Cerebras (throughput priority)

  • 60 seconds or training: H100 (flexibility priority)

This minimizes latency-sensitive user impact while optimizing cost for batch operations.

See Lambda GPU pricing and OpenAI API pricing for comparison baselines.

FAQ

Is Groq's latency advantage worth the cost premium? For user-facing applications, yes. <1-second TTFT meaningfully improves experience. For batch processing, no. Throughput metrics matter more than latency.

Can I achieve Groq latency on H100 GPUs? Approximately. H100 inference servers with aggressive optimization reach 1000-1500ms TTFT. Groq reaches 400-600ms. Optimization gap: 40-60% faster on Groq.

Does Cerebras require long-term commitments? Yes, minimum 1-year commitments typical. Groq cloud API requires no commitments (pay-per-use).

Can I use Groq for code generation? Yes, Llama 3.1 70B on Groq handles code generation adequately. Quality comparable to GPU inference.

What's the environmental impact of each approach? Groq's specialized architecture uses 40-60% less power than GPUs for equivalent throughput. Cerebras's massive scale achieves 10x power efficiency per TFLOP. Both substantially more efficient than GPU inference.

Can I self-host Groq or Cerebras? Groq: Limited on-premise deployment available through partners. Cerebras: Full on-premise deployment available, requires dedicated infrastructure and sales engagement.

Neither offers traditional cloud rental approach like RunPod/Lambda.

How does Groq handle model fine-tuning? Groq does not support model fine-tuning. Inference-only hardware architecture precludes training.

Should I prepay for Groq tokens? No prepaid options available. Pay-per-use only. Budget based on consumption estimates.

Sources

  • Groq official documentation and API pricing
  • Cerebras systems specifications (CS-3 datasheet)
  • Benchmark results from Groq technical reports
  • LLM inference latency measurements (March 2026)
  • Independent benchmarking studies on LPU vs GPU performance