Karnak 40B v1.0 — Hardware Requirements & GPU Compatibility
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Karnak 40B v1.0 is a 40.7B-parameter open language model from Applied-Innovation-Center. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 24.84 GB of VRAM — see which GPUs and Macs can run it below.
81.0K downloads
36 likesSpecifications
- Publisher
- Applied-Innovation-Center
- Parameters
- 40.7B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 192,728
- Release Date
- 2026-02-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Karnak 40B v1.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 17.7 GB | 34.8 GB | 17.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 20.3 GB | 37.3 GB | 19.83 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 24.8 GB | 41.9 GB | 24.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 29.4 GB | 46.5 GB | 28.98 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 34.0 GB | 51.0 GB | 33.55 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 41.1 GB | 58.1 GB | 40.67 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 81.8 GB | 98.8 GB | 81.34 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Karnak 40B v1.0?
Q4_K_M · 24.8 GBKarnak 40B v1.0 (Q4_K_M) requires 24.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 33+ GB is recommended. Using the full 262K context window can add up to 17.0 GB, bringing total usage to 41.9 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Karnak 40B v1.0?
Q4_K_M · 24.8 GB15 devices with unified memory can run Karnak 40B v1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does Karnak 40B v1.0 need?
Karnak 40B v1.0 requires 24.8 GB of VRAM at Q4_K_M, or 81.8 GB at BF16. Full 262K context adds up to 17.0 GB (41.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 40.7B × 4.8 bits ÷ 8 = 24.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M24.8 GBQ4_K_M + full context41.9 GB- Can NVIDIA GeForce RTX 4090 run Karnak 40B v1.0?
Yes, at Q3_K_M (20.3 GB) or lower. Higher quantizations like Q4_K_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Karnak 40B v1.0?
For Karnak 40B v1.0, Q4_K_M (24.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (29.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 17.7 GB.
VRAM requirement by quantization
Q2_K17.7 GBQ4_K_M ★24.8 GBQ5_K_M29.4 GBQ6_K34.0 GBQ8_041.1 GBBF1681.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Karnak 40B v1.0 on a Mac?
Karnak 40B v1.0 requires at least 17.7 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Karnak 40B v1.0 locally?
Yes — Karnak 40B v1.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 24.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Karnak 40B v1.0?
At Q4_K_M, Karnak 40B v1.0 can reach ~117 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 24.8 × 0.55 = ~117 tok/s
Estimated speed at Q4_K_M (24.8 GB)
~117 tok/s~88 tok/s~73 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Karnak 40B v1.0?
At Q4_K_M, the download is about 24.40 GB. The full-precision BF16 version is 81.34 GB. The smallest option (Q2_K) is 17.28 GB.
- Which GPUs can run Karnak 40B v1.0?
1 consumer GPU can run Karnak 40B v1.0 at Q4_K_M (24.8 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Karnak 40B v1.0?
15 devices with unified memory can run Karnak 40B v1.0 at Q4_K_M (24.8 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.