Q4 vs Q5 vs Q8: which quantization for Llama 70B in 2026?
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Damien · LocalIAVRAM table per quant (Q3 to FP16), measured quality loss (Δ perplexity), GPU recommendations and estimated tok/s per setup. No bullshit.
Quantization shrinks an LLM's memory footprint by 2× to 4× while trimming weight precision. For Llama 3.3 70B, it decides whether the model fits on your card and how much quality you sacrifice. Here is how to choose between Q4, Q5 and Q8 without bullshit.
The table that settles it
| Q3_K_M | ~32 GB | +5-8% perplexity | Noticeable: grammar OK, reasoning degraded |
| Q4_K_M | ~44 GB | +2-3% | Good: consumer sweet spot |
| Q5_K_M | ~52 GB | +0.8-1.2% | Very good: indistinguishable in chat |
| Q6_K | ~60 GB | +0.3-0.5% | Near-perfect |
| Q8_0 | ~78 GB | +0.05-0.1% | Indistinguishable from FP16 |
| FP16 | ~146 GB | 0 | Reference, datacenter only |
Which quant for which GPU?
- 24 GB (RTX 4090): Llama 70B does not fit in single GPU at any reasonable quant. Pick a 27-32B model instead.
- 32 GB (RTX 5090): Llama 70B Q3_K_M just fits (~30 GB). Quality slightly degraded.
- 48 GB (A6000, 6000 Ada): Llama 70B Q4_K_M fits with margin. Realistic minimum for single-GPU 70B.
- 64 GB (2× 5090): Llama 70B Q5_K_M or Q6_K, near-FP16 quality.
- 96 GB+ (2× A6000 NVLink, H100, MI300X): Q8 or FP16 for production-critical workloads.
Speed tradeoff (single user on A6000)
| Q3_K_M | ~17-19 tok/s | Less bytes to stream |
| Q4_K_M | ~14-16 tok/s | Speed/quality sweet spot |
| Q5_K_M | ~12-14 tok/s | +10% bytes vs Q4 |
| Q8_0 | ~9-11 tok/s | 2× the weight |
| FP16 | ~5-6 tok/s | 4× the weight |
Default 2026 combo: Llama 3.3 70B Q5_K_M on a 2× RTX 5090 rig (64 GB VRAM). Quality indistinguishable from FP16 in chat/RAG, 12-15 tok/s per stream, 32k context usable without saturating VRAM.
Open the calculator / request a quote with your target model, users and constraints.
QuantizationLlamaOptimization