Can you run K2 Think V2 locally?
K2 family · 2026 · 72.6B parameters (72.6B activated per token) · Hugging Face model card
K2 Think V2 is a dense open-weights reasoning model fine-tuned from K2-V2-Instruct (vendor card: 70 billion parameter model; HF safetensors.total=72,550,195,200). Native context is 262,144 tokens per config max_position_embeddings; the vendor serving table lists 131,072 with a 2x YaRN extension reaching the same 262,144. Architecture is llama-style GQA, 80 layers, 8 KV heads. Weights are stored in float32, so a full-precision checkout is ~290 GB. Local VRAM fit at Q4-class is roughly 48 GB (calculated). Measured speeds: not yet published here.
Minimum: 48 GB+ memory (Q4-class on 72.55B dense, calculated) · Recommended: 64 GB+ for comfortable context headroom (Q4-class on 72.55B dense, calculated)
How much VRAM does K2 Think V2 need at each quantization?
K2 Think V2 needs 49.2 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 43.5 GB | 49.2 GB | 58.6 GB |
| Q5_K_M | 5.7 | 51.7 GB | 58.2 GB | 67.6 GB |
| Q6_K | 6.6 | 59.9 GB | 67.2 GB | 76.6 GB |
| Q8_0 | 8.5 | 77.1 GB | 86.1 GB | 95.5 GB |
| FP16 | 16 | 145.1 GB | 161.0 GB | 170.4 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (80 layers, 8 KV heads, 128 head dim). Source: HF-verified 2026-09-23 (rechecked 2026-09-27) via api (gated=false private=false params=72.55B safetensors.total=72550195200 downloadable_safetensors_sum=290200865568 ctx=262144 license=apache-2.0 repo=IFM/K2-Think-V2@f86e5d11c501ad19d3b26e66b0ac550fc134c628 layers=80 hidden=8192 kv_heads=8 dtype=float32). dense_or_moe=dense num_experts=None num_experts_per_tok=None active_params_b=72.55 (vendor card; VRAM fit uses active, not dense-equivalent). VRAM figures are documented calculations (Q4 ~0.6GB/B), not measurements. No tok/s invented.
Which GPUs can run K2 Think V2 locally?
At Q4_K_M with a 4k context, K2 Think V2 needs 49.2 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs K2 Think V2 comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Needs 2+ GPUs to run K2 Think V2
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 49.2 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7800 | 32 GB ×2 | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB ×2 | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB ×2 | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB ×2 | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB ×2 | 1555 GB/s | Data Center GPU | |
| AMD Radeon Pro W7900 | 48 GB ×2 | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB ×2 | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB ×2 | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB ×2 | 768 GB/s | Pro GPU | Check price |
Can a Mac run K2 Think V2?
Yes — these Apple Silicon machines fit K2 Think V2 at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M5 Max (Mac Studio) | 128 GB | 96 GB | — |
| M5 Ultra (Mac Studio) | 512 GB | 384 GB | 1200 GB/s |
Can K2 Think V2 run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for K2 Think V2 at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run K2 Think V2?
These mini PCs and workstations from our database fit K2 Think V2 at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.