Card × model benchmark
Qwen2.5 Coder 1.5B on RTX 5070 Ti Q4_K_M
SmoothOn an RTX 5070 Ti (16 GB), Qwen2.5 Coder 1.5B in Q4_K_M generates 481 tokens/s and reads the prompt at 26 520 tokens/s, using 2,3 Go of video memory.
Median of 1 run · latest on 25/09/26
481 tok/s
Generation
26 520 tok/s
Prompt processing
2,3 Go
VRAM used (peak)
73 W
Average power
6,59 tok/s/W
Efficiency
0,01 €/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 16 GB? | Estimated speed |
|---|---|---|---|---|
| 4 096 | 0,1 Go | 2,4 Go | yes | ≈ 440 tok/s |
| 8 192 | 0,2 Go | 2,5 Go | yes | ≈ 400 tok/s |
| 16 384 | 0,4 Go | 2,7 Go | yes | ≈ 338 tok/s |
| 32 768 | 0,9 Go | 3,2 Go | yes | ≈ 258 tok/s |
KV cache per token = 2 × 28 layers × 2 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”
Other models measured on the RTX 5070 Ti
All measurements
| Date | Source | Type | Generation | Prompt | VRAM | Max temp. | Power | llama.cpp |
|---|---|---|---|---|---|---|---|---|
| 25/09/26 | French_Girl_13 | context 32 768MSI Gaming X Trio | 299 | 9 168 | 3,5 Go | 67 °C | 161 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | context 16 384MSI Gaming X Trio | 364 | 13 548 | 3,0 Go | 61 °C | 112 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | context 8 192MSI Gaming X Trio | 408 | 17 709 | 2,8 Go | 54 °C | 106 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | standardMSI Gaming X Trio | 481 | 26 520 | 2,3 Go | 53 °C | 73 W | llama.cpp 96278e39f |
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