Q4 vs Q5 vs Q8: welke quantization voor Llama 70B in 2026?
DO
Damien · LocalIAVRAM-tabel per quant (Q3 tot FP16), gemeten kwaliteitsverlies, GPU-aanbevelingen en geschatte tok/s per setup.
Vertaald artikel. Deze versie is gelokaliseerd zodat internationale pagina's geen Franse artikeltekst tonen. Technische data, prijzen en adviezen blijven gelijk.
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 de calculator / vraag ons om advies met doelmodel, gebruikers en randvoorwaarden.
Veelgestelde vragen
Which quantization for Llama 3.3 70B locally?+
Q5_K_M is the production sweet spot: 52 GB VRAM, quality loss below 1.5% (perplexity), indistinguishable from FP16 in chat. Q4_K_M (44 GB) if VRAM is limited. Q8 (78 GB) only for critical production.
What quality difference between Q4 and Q8?+
Q4_K_M: +2-3% perplexity vs FP16, noticeable in complex reasoning. Q5_K_M: +0.8-1.2%, indistinguishable in everyday chat. Q8: +0.05-0.1%, indistinguishable in benchmarks. For 95% of business cases, Q5_K_M is plenty.
Q4_K_M or Q4_K_S, which variant?+
K_M (Medium) by default: a quality/size balance. K_S (Small): -5% VRAM, -1% quality (aggressive). K_L (Large): +5% VRAM, +0.5% quality (cautious). For the vast majority, K_M is the right pick.
Is FP16 really worth it versus Q5_K_M?+
No for local production. FP16 = 146 GB VRAM for 70B (datacenter only). For 0.8% extra quality you triple the VRAM. Q5_K_M on 2x RTX 5090 (64 GB) covers 99% of real needs.
How do you test a quantization's quality on your use case?+
Compare Q4_K_M, Q5_K_M and Q8 on 20-30 prompts representative of your field. Criteria: coherence, factual accuracy, language quality. The perceived difference between Q5 and Q8 is usually small.
QuantizationLlamaOptimalisatie