Card × model benchmark
Meta Llama 3.1 8B Instruct on RTX 4070 Q4_K_M
SmoothOn an RTX 4070 (12 GB), Meta Llama 3.1 8B Instruct in Q4_K_M generates 82,3 tokens/s and reads the prompt at 4 336 tokens/s, using 6,8 Go of video memory.
Median of 1 run · latest on 04/08/26
82,3 tok/s
Generation
4 336 tok/s
Prompt processing
6,8 Go
VRAM used (peak)
138 W
Average power
0,60 tok/s/W
Efficiency
0,12 €/M tok
Electricity / M tokens · 0,25 €/kWh
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What about a longer context?
An estimate, not a measurement: the KV cache grows with context, takes VRAM and slows generation down.
| Context | KV cache | Estimated VRAM | Fits in 12 GB? | Estimated speed |
|---|---|---|---|---|
| 4 096 | 0,5 Go | 7,2 Go | yes | ≈ 75,8 tok/s |
| 8 192 | 1,0 Go | 7,7 Go | yes | ≈ 69,3 tok/s |
| 16 384 | 2,0 Go | 8,7 Go | yes | ≈ 59,1 tok/s |
| 32 768 | 4,0 Go | 10,7 Go | yes | ≈ 45,8 tok/s |
| 65 536 | 8,0 Go | 14,7 Go | no | — |
| 131 072 | 16,0 Go | 22,7 Go | no | — |
KV cache per token = 2 × 32 layers × 8 KV heads × 128 × 2 bytes (f16). Estimated VRAM = measured VRAM + added KV cache. Speed ≈ measured speed × weights ÷ (weights + added KV cache) — attention compute is not modelled, the real drop is slightly larger. Fine-tune it in the “Configurator”
Meta Llama 3.1 8B Instruct on other cards
| Card | VRAM | Generation | Difference | Price |
|---|---|---|---|---|
| RTX 5070 Ti | 16 Go | 151 tok/s | +83,7 % | 1 400 € |
| RTX 4090demo | 24 Go | 132 tok/s | +60,5 % | — |
| RTX 5080demo | 16 Go | 124 tok/s | +50,8 % | 1 850 € |
| RTX 4080demo | 16 Go | 103 tok/s | +25,2 % | 780 € |
| RTX 4070 Ti | 12 Go | 89,6 tok/s | +8,9 % | 927 € |
| RTX 4070 | 12 Go | 82,3 tok/s | this card | 595 € |
| RTX 3060demo | 12 Go | 48,0 tok/s | −41,6 % | 260 € |
Other models measured on the RTX 4070
All measurements
| Date | Source | Type | Generation | Prompt | VRAM | Max temp. | Power | llama.cpp |
|---|---|---|---|---|---|---|---|---|
| 04/08/26 | measured | standard | 82,3 | 4 336 | 6,8 Go | 68 °C | 138 W | llama.cpp 935cad649 |
Protocol: llama-bench (llama.cpp), prompt 512 + generation 128 tokens, model fully loaded in VRAM. VRAM used = system peak (includes the OS, ~0.5 to 1 GB more than the model alone). Measure my card