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LLaMA 3.1 in April 2026
LLaMA 3.1 remains the most widely deployed open-source LLM in April 2026. While newer models like DeepSeek V3 score higher on benchmarks, LLaMA 3.1 70B’s combination of strong general-purpose quality, efficient single-model deployment, and battle-tested stability makes it the workhorse of self-hosted LLM deployments. This report covers current performance data on GigaGPU dedicated servers.
70B Model Throughput by GPU
LLaMA 3.1 70B via vLLM at 10 concurrent users:
| GPU Configuration | Precision | Total tok/s | First Token | VRAM Used |
|---|---|---|---|---|
| 1x RTX 5090 | Q4 (AWQ) | 62 | 145 ms | 22 GB |
| 1x RTX 5090 | Q4 (AWQ) | 88 | 110 ms | 22 GB |
| 2x RTX 5090 | FP16 | 85 | 120 ms | 42 GB |
| 1x RTX 6000 Pro 96 GB | FP16 | 95 | 105 ms | 68 GB |
| 1x RTX 3090 | Q4 (AWQ) | 35 | 210 ms | 22 GB |
| 1x RTX 6000 Pro | Q4 (AWQ) | 48 | 175 ms | 22 GB |
The RTX 5090 running Q4-quantised LLaMA 70B remains the best value option for production inference. Full benchmark data at the tokens per second benchmark.
8B Model Throughput by GPU
LLaMA 3.1 8B in FP16 at 10 concurrent users:
| GPU | Total tok/s | First Token | VRAM Used |
|---|---|---|---|
| RTX 3090 | 125 | 55 ms | 16 GB |
| RTX 5090 | 195 | 38 ms | 16 GB |
| RTX 5090 | 248 | 28 ms | 16 GB |
The 8B model is extremely fast on consumer hardware, making it ideal for latency-sensitive applications where the quality ceiling is acceptable. An RTX 3090 delivers 125 tok/s, more than enough for interactive chatbot applications.
Quantisation Analysis
LLaMA 3.1 70B quality retention under quantisation:
| Precision | MMLU | HumanEval | VRAM (model only) | Speed (RTX 5090) |
|---|---|---|---|---|
| FP16 | 82.0 | 72.5 | 140 GB | Requires 2+ GPUs |
| Q8 (GPTQ) | 81.5 | 71.8 | 72 GB | Requires 2 GPUs |
| Q4 (AWQ) | 80.8 | 70.2 | 38 GB | 62 tok/s (1 GPU) |
| Q3 | 78.5 | 66.8 | 30 GB | 68 tok/s (1 GPU) |
Q4 is the sweet spot: only 1.2 MMLU points below FP16 while fitting on a single RTX 5090. For detailed quality analysis, see the quantised vs full precision comparison.
Optimal Deployment Configurations
Based on April 2026 testing, the recommended configurations for LLaMA 3.1 are:
| Use Case | Model Size | GPU | Engine |
|---|---|---|---|
| Development / prototyping | 8B FP16 | RTX 3090 | Ollama |
| Production chatbot | 70B Q4 | RTX 5090 | vLLM |
| High-quality production | 70B FP16 | 2x RTX 5090 | vLLM |
| Maximum accuracy | 405B Q4 | Multi-GPU | vLLM |
Deploy LLaMA 3.1 on Dedicated Hardware
The most popular open-source LLM on your own GPU server. Proven stability, excellent performance, predictable monthly cost.
Browse GPU ServersPerformance Verdict
LLaMA 3.1 70B delivers reliable, well-understood performance that makes it the default choice for production deployments in April 2026. It may not top every benchmark, but its combination of quality, speed, community support, and tooling compatibility is unmatched. For teams prioritising production stability over bleeding-edge scores, LLaMA 3.1 is the recommended model.
Compare with DeepSeek V3 for higher quality at higher hardware cost, and Qwen 2.5 for multilingual use cases. For cost projections, use the cost per million tokens calculator.
