KAT-Coder V2.5-Dev VRAM requirements
Memory needed to run KAT-Coder V2.5-Dev at every published size and quant, with an 8k context. Each figure is weights plus KV cache plus runtime buffers, from the same memory model as the VRAM calculator.
Total memory by size and quant
GB at an 8k context. Bold is the Q4_K_M column, the usual starting point.
| Size | Q8_0 | Q6_K | Q5_K_M | Q4_K_M | Q3_K_M |
|---|---|---|---|---|---|
| 35B · 3B active | 37 | 30 | 26 | 22 | 17 |
KV cache by context length
GB the context alone adds on top of the weights. Add this to a weights figure to size a longer session.
| Size | Weights at Q4_K_M | 4k ctx | 16k ctx | 32k ctx | 128k ctx |
|---|---|---|---|---|---|
| 35B · 3B active | 20 | 0.1 | 0.3 | 0.7 | 2.7 |
What each size fits on at Q4_K_M
Hardware whose memory holds the model with headroom, smallest pool first. Fits means under 85% of the pool at an 8k context. A Mac's pool is what macOS lets the GPU use - two thirds of its memory up to 32 GB, three quarters from 36 GB - not the whole of it.
- 35B · 3B activeneeds about 22 GB
Smallest: AMD MI50 32GB (32 GB), AMD Radeon AI PRO R9700 32GB (32 GB), AMD Radeon Pro V620 32GB (32 GB), AMD Radeon Pro W6800 (32 GB), and 41 more.
On a Mac: 36 GB or more, with macOS's default GPU limit.
Frequently asked
How much VRAM does KAT-Coder V2.5-Dev need?
About 22 GB at Q4_K_M with an 8k context for the 35B · 3B active model, including the KV cache and runtime buffers. Q8 needs more and Q3 less; the table above lists every rung.
Can I run KAT-Coder V2.5-Dev on a 16GB GPU?
Not comfortably. Even the 35B · 3B active model needs about 22 GB at Q4_K_M with an 8k context, which is more than a 16 GB card holds with headroom. A lower quant or CPU offloading can still get it running, at a cost in quality or speed.
Can I run KAT-Coder V2.5-Dev on a 24GB GPU such as an RTX 3090 or 4090?
Not comfortably. Even the 35B · 3B active model needs about 22 GB at Q4_K_M with an 8k context, which is more than a 24 GB card holds with headroom.
How are these numbers calculated?
Weights at the quant's bits per weight, plus a KV cache sized from the model's attention design for the chosen context, plus compute buffers and a fixed runtime allowance. It is the same memory model the VRAM calculator uses. A family whose attention profile is not on file is sized as plain grouped-query attention, which errs towards needing more memory.