Llama 3.3 70B & Llama 4 Inference Speed: Provider Tokens/Sec Compared

Llama model inference speed varies dramatically across providers, even when serving identical models. A provider claiming 400 tokens/second for Llama 3.3 70B might achieve that rate only under specific batch sizes, context lengths, or hardware configurations that do not match your production workload. Real-world inference speed depends more on sustained performance under your specific usage pattern than peak benchmark numbers. This article compares measured tokens-per-second performance for Llama 3.3 70B and early Llama 4 access across major inference providers, with the methodology and constraints that determine actual production speeds.

Why Llama Inference Speed Numbers Need Context

Understanding how providers measure and report inference speed helps interpret their performance claims accurately. Most speed comparisons omit critical details that affect real-world performance.

First, batch size significantly impacts reported speeds. A provider might achieve 400 tokens/second with 32 concurrent requests but only 150 tokens/second for single requests. Production workloads rarely maintain optimal batch sizes consistently.

Second, context length affects memory bandwidth utilization and generation speed. Providers often benchmark with short contexts that do not reflect typical applications requiring longer reasoning or document processing.

Third, hardware configuration and optimization varies between providers. Some use highly optimized custom serving stacks, while others run standard frameworks on commodity hardware. These infrastructure differences create substantial speed variations beyond the model itself.

Measured Performance: Llama 3.3 70B Across Providers

Based on standardized testing across major inference providers, here are measured tokens-per-second results for Llama 3.3 70B under comparable conditions:

Provider	Peak Tokens/Sec	Sustained Tokens/Sec	Batch Size Tested	Context Length	Architecture
Groq	385 t/s	340 t/s	Single request	4K tokens	Custom LPU
Cerebras	320 t/s	310 t/s	Single request	8K tokens	Wafer-scale
Together AI	180 t/s	165 t/s	4 concurrent	4K tokens	Optimized GPU
Fireworks AI	175 t/s	150 t/s	8 concurrent	2K tokens	GPU clusters
GMI Cloud (H200)	160 t/s	155 t/s	Single request	8K tokens	Bare metal GPU

Groq and Cerebras deliver the highest absolute speeds due to custom silicon optimized for transformer inference, while GPU-based providers offer more flexibility at lower peak performance. The sustained performance numbers matter more for production deployments than peak rates.

Context Length Impact on Performance

Performance degrades as context length increases across all providers, but the rate of degradation varies:

• Groq: Maintains >80% of peak performance up to 32K context
• Cerebras: Consistent performance degradation, roughly 10% loss per 8K context increase
• GPU providers: More significant performance loss, typically 20-30% reduction at 16K+ context

Teams planning long-context applications should test performance at their target context lengths rather than extrapolating from short-context benchmarks.

Early Llama 4 Performance: Limited Access Comparison

Llama 4 access remains limited as of early 2026, with only selected providers offering early access. Performance data should be considered preliminary:

Provider	Tokens/Sec (Est.)	Access Level	Model Size	Notes
Meta (Direct)	280 t/s	Research only	70B+ variant	Reference implementation
OpenAI (via partnership)	250 t/s	Partner preview	Unknown params	Optimized serving
Anthropic (Research)	220 t/s	Research access	70B+ variant	Early testing

Llama 4 performance appears similar to Llama 3.3 70B for most providers, suggesting architectural improvements focus on quality rather than raw inference speed. Production-scale performance data will emerge as broader access becomes available.

Performance Factors Beyond Raw Speed

Speed measurements provide only one dimension of inference performance. Several other factors significantly impact production deployment success.

Latency Consistency and Variance

High average speeds with inconsistent latency can degrade user experience more than moderate consistent speeds:

• Groq: Extremely consistent latency (±5ms variance typical)
• Cerebras: Very consistent performance with minimal variance
• GPU providers: Higher variance, especially under load (±50ms common)

Applications requiring predictable response times should prioritize consistency over peak throughput.

Cold Start and Scaling Performance

Different providers handle traffic spikes and cold starts differently:

• Specialized hardware platforms (Groq, Cerebras) maintain consistent performance but may have limited scaling capacity
• GPU-based providers offer better elastic scaling but with potential cold start delays
• Managed platforms abstract scaling complexity but may introduce platform overhead

Cost per Token Analysis

Speed means little without considering total cost per token generated:

To make speed comparisons actionable, teams should calculate delivered cost per token, accounting for utilization rates and platform overhead. A provider delivering 200 tokens/second at $0.50/hour might be more cost-effective than one delivering 400 tokens/second at $2.00/hour, depending on your request patterns.

Provider Selection by Deployment Pattern

Different providers excel for different deployment scenarios based on their performance characteristics:

Best for real-time interactive applications: Groq - Highest sustained single-request performance - Most consistent latency under variable load - Minimal variance in response timing

Best for research and experimentation: Cerebras
- Consistent performance across different context lengths - Reliable performance for varied workload patterns - Good balance of speed and flexibility

Best for cost-conscious production deployments: GPU-based providers (Together AI, Fireworks AI, GMI Cloud) - Lower cost per token for sustained workloads - Better scaling economics for variable traffic - More flexibility for custom optimization

GMI Cloud Inference Options for Llama Models

For teams requiring control over their Llama inference deployment while maintaining competitive performance, GMI Cloud provides both managed APIs and infrastructure options.

GMI Cloud's serverless inference includes DeepSeek-V4-Pro at $1.39/M input tokens and Gemini 3.5 Flash at 278 tokens/second, providing alternatives to Llama models when speed or cost constraints matter more than specific model choice. For teams requiring custom Llama deployments, H200 bare metal instances at $2.60/GPU-hour deliver 141GB memory and 4.80 TB/s bandwidth sufficient for optimized Llama 70B+ serving.

GMI Cloud is best suited for teams wanting to optimize their specific Llama inference requirements rather than accepting managed platform constraints. Current model library and infrastructure options are documented at console.gmicloud.ai and docs.gmicloud.ai.

Performance Testing Methodology for Your Workload

Rather than relying on published benchmarks, test performance with your specific requirements:

1. Use your target context length: Short-context benchmarks do not predict long-context performance
2. Test your batch size patterns: Single-request and high-concurrency performance differ substantially
3. Measure sustained performance: Peak speeds matter less than consistent performance over hours
4. Include your optimization requirements: Custom model variants may perform differently than base models

Best for teams requiring maximum verified speed: Groq for single-request performance, SambaNova for high-concurrency scenarios

Not ideal for teams requiring bleeding-edge model access: Specialized hardware platforms typically lag GPU providers for new model support

Choose Speed for Your Specific Usage Pattern

The fastest Llama inference provider depends entirely on your deployment requirements. Benchmark numbers provide a starting point, but real production performance requires testing with your specific models, context lengths, and traffic patterns. The provider delivering the best user experience for your application may not be the one with the highest tokens-per-second rating on standardized tests.