Card × model benchmark
Qwen2.5 Coder 14B on RTX 5070 Ti Q4_K_M
SmoothOn an RTX 5070 Ti (16 GB), Qwen2.5 Coder 14B in Q4_K_M generates 81,9 tokens/s and reads the prompt at 3 725 tokens/s, using 9,4 Go of video memory.
Median of 1 run · latest on 25/09/26
81,9 tok/s
Generation
3 725 tok/s
Prompt processing
9,4 Go
VRAM used (peak)
166 W
Average power
0,49 tok/s/W
Efficiency
0,14 €/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 | 0,8 Go | 10,0 Go | yes | ≈ 76,5 tok/s |
| 8 192 | 1,5 Go | 10,8 Go | yes | ≈ 71,0 tok/s |
| 16 384 | 3,0 Go | 12,3 Go | yes | ≈ 62,0 tok/s |
| 32 768 | 6,0 Go | 15,3 Go | yes | ≈ 49,5 tok/s |
KV cache per token = 2 × 48 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”
Qwen2.5 Coder 14B on other cards
| Card | VRAM | Generation | Difference | Price |
|---|---|---|---|---|
| RTX 5070 Ti | 16 Go | 81,9 tok/s | this card | — |
| RTX 4070 Ti | 12 Go | 50,3 tok/s | −38,6 % | — |
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 | 50,2 | 1 266 | 15,5 Go | 70 °C | 225 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | context 16 384MSI Gaming X Trio | 62,9 | 2 019 | 12,7 Go | 70 °C | 206 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | context 8 192MSI Gaming X Trio | 71,6 | 2 670 | 11,2 Go | 67 °C | 197 W | llama.cpp 96278e39f |
| 25/09/26 | French_Girl_13 | standardMSI Gaming X Trio | 81,9 | 3 725 | 9,4 Go | 61 °C | 166 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