KAT-Coder-V2.5-Dev · 10 GPUs measured first-party · llama.cpp · Updated October 2026
KAT-Coder-V2.5-Dev on 10 GPUs, measured first-party: NVIDIA RTX PRO 6000 Blackwell Workstation Edition leads at 253.3 tok/s, L4 trails at 79 tok/s, and it peaked at 22GB of VRAM.
Benchmarked weights: bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF

253.3 tok/s on KAT-Coder-V2.5-Dev, the ceiling. Measured on our bench. 96GB of VRAM, $8,565 at launch.
What GPU Do You Need for KAT-Coder-V2.5-Dev?, tok/s by GPU
Efficiency: tok/s per 100W drawn
Power is the average pulled during the run, sampled at 1Hz. The fastest card is often not the one here, and for anything left running this is the number that shows up on the bill.
Value: tok/s per $1,000 of MSRP
Launch price, not street price, so it ages. A speed leaderboard always crowns the most expensive card; this is the counterweight.
KAT-Coder-V2.5-Dev. Measured tokens per second by GPU
| GPU | tok/s | Prompt t/s | tok/W | Avg power |
|---|---|---|---|---|
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 253.3 | 8044 | 1.67 | 151.7 W |
| NVIDIA B300 | 239.7 | 4220.4 | 0.83 | 287.2 W |
| NVIDIA H200 | 236.3 | 6167.7 | 1.45 | 162.9 W |
| NVIDIA H100 80GB HBM3 | 231.8 | 6144.3 | 1.4 | 166.1 W |
| NVIDIA B200 | 230.7 | 6547.4 | 0.78 | 295.9 W |
| NVIDIA L40S | 173.1 | 6738.3 | 1.3 | 133.0 W |
| NVIDIA A100 80GB SXM4 | 146.4 | 3324.3 | 1.02 | 143.7 W |
| NVIDIA A100 40GB SXM4 | 137.3 | 3220.5 | 1.29 | 106.7 W |
| NVIDIA A10G | 113.6 | 2152.8 | 1.31 | 86.5 W |
| NVIDIA L4 | 79.01 | 1985.8 | 1.54 | 51.4 W |
What the numbers show. Across 10 GPUs measured on our own bench, RTX PRO 6000 Blackwell Workstation Edition is fastest at 253 tok/s. The slowest, L4, manages 79.0, so the spread is 3.2x from top to bottom. Per dollar of launch price, A10G gives the most (40.6 tok/s per $1,000).
How it compares. H100 80GB HBM3: KAT-Coder-V2.5-Dev 231.8 tok/s, Nemotron Nano 9B v2 227.7 (9B), Qwen3.6 35B A3B 236.7 (36B), Ornith 1.5 35B A3B 240.2 (36B), Ornith-1.0-35B 221.7. 2 of 4 beat KAT-Coder-V2.5-Dev here. Qwen3.6 35B A3B and Ornith 1.5 35B A3B are mixture-of-experts, so per token they compute only a slice of their size.
Cost on a rented GPU. 1M generated tokens of KAT-Coder-V2.5-Dev: $0.95 on a A100 40GB SXM4 ($0.47/hr, 2.0 hours), $1.18 on a RTX PRO 6000 Blackwell Workstation Edition ($1.08/hr, 66 min, 1.2x the cost).
KAT-Coder-V2.5-Dev: cost per 1M generated tokens on rented GPUs
| GPU | Cheapest rate | Speed (tok/s) | Cost per 1M generated tokens |
|---|---|---|---|
| NVIDIA A100 40GB SXM4 | $0.47/hr | 137.3 | $0.95 |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | $1.08/hr | 253.3 | $1.18 |
| NVIDIA L40S | $0.79/hr | 173.1 | $1.27 |
| NVIDIA L4 | $0.44/hr | 79.01 | $1.55 |
| NVIDIA A100 80GB SXM4 | $0.95/hr | 146.4 | $1.80 |
| NVIDIA H100 80GB HBM3 | $2.14/hr | 231.8 | $2.56 |
| NVIDIA H200 | $3.59/hr | 236.3 | $4.22 |
| NVIDIA B200 | $5.98/hr | 230.7 | $7.20 |
| NVIDIA B300 | $6.94/hr | 239.7 | $8.04 |
Cheapest hourly rate we track on RunPod and Vast.ai, divided by the measured speed. Startup time and storage are extra.
Speed tiers for KAT-Coder-V2.5-Dev. 30+ tok/s: 10 (RTX PRO 6000 Blackwell Workstation Edition, B300, H200). 30 tok/s is roughly where replies outpace reading.
Reading your prompt. Before KAT-Coder-V2.5-Dev writes anything it reads the input: 8044.0 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (0.5s for a 4,000-token prompt), 1985.8 on the L4 (2.0s). Long documents and big code files feel this number more than the generation speed.
VRAM for KAT-Coder-V2.5-Dev. Measured peak 20.9GB, so 24GB is the smallest common card size; smallest card it ran on: A10G (24GB). With long context: Q4_K_M 27GB (tested), Q2_K 16GB, Q3_K_M 21GB, Q5_K_M 32GB, Q6_K 38GB.
Power on KAT-Coder-V2.5-Dev. Most efficient: RTX PRO 6000 Blackwell Workstation Edition, 152W, 0.17 kWh per 1M generated tokens. Hungriest: B200, 296W, 0.36 kWh. At $0.15/kWh: $0.025 per 1M generated tokens.
Fastest on KAT-Coder-V2.5-Dev: NVIDIA RTX PRO 6000 Blackwell Workstation Edition, 253.3 tok/s. Cheapest to rent per job: NVIDIA A100 40GB SXM4, $0.95 per 1M generated tokens.
llama.cpp llama-bench at Q4_K_M, 512-token prompt and 128 generated tokens, three runs after a warmup, full GPU offload, with power and VRAM sampled throughout. Token generation is memory-bandwidth-bound, so the ranking tracks bandwidth closely, which makes it a fair guide to cards we haven't run yet.