Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER — Hardware Requirements & GPU Compatibility
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Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER is a 42.4B-parameter open language model from DavidAU in the Qwen 3 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 25.86 GB of VRAM — see which GPUs and Macs can run it below.
1.9K downloads
35 likesSpecifications
- Publisher
- DavidAU
- Family
- Qwen 3
- Parameters
- 42.4B
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-08-21
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 18.4 GB | 36.3 GB | 18.01 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 21.1 GB | 38.9 GB | 20.66 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 25.9 GB | 43.7 GB | 25.42 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 30.6 GB | 48.5 GB | 30.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 35.4 GB | 53.2 GB | 34.96 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 42.8 GB | 60.7 GB | 42.37 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 85.2 GB | 103.0 GB | 84.74 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 Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
Q4_K_M · 25.9 GBQwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER (Q4_K_M) requires 25.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 34+ GB is recommended. Using the full 262K context window can add up to 17.9 GB, bringing total usage to 43.7 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 Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
Q4_K_M · 25.9 GB32 devices with unified memory can run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER need?
Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER requires 25.9 GB of VRAM at Q4_K_M, or 85.2 GB at BF16. Full 262K context adds up to 17.9 GB (43.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 42.4B × 4.8 bits ÷ 8 = 25.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 18.3 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M25.9 GBQ4_K_M + full context43.7 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
Yes, at Q3_K_M (21.1 GB) or lower. Higher quantizations like Q4_K_M (25.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
For Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER, Q4_K_M (25.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (30.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 18.4 GB.
VRAM requirement by quantization
Q2_K18.4 GBQ4_K_M ★25.9 GBQ5_K_M30.6 GBQ6_K35.4 GBQ8_042.8 GBBF1685.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER on a Mac?
Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER requires at least 18.4 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 Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER locally?
Yes — Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER can run locally on consumer hardware. At Q4_K_M quantization it needs 25.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
At Q4_K_M, Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER can reach ~170 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 25.9 × 0.65 = ~201 tok/s
Estimated speed at Q4_K_M (25.9 GB)
~201 tok/s~201 tok/s~170 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
At Q4_K_M, the download is about 25.42 GB. The full-precision BF16 version is 84.74 GB. The smallest option (Q2_K) is 18.01 GB.
- Which GPUs can run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
1 consumer GPU can run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER at Q4_K_M (25.9 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER?
35 devices with unified memory can run Qwen3 42B A3B 2507 Thinking Abliterated Uncensored TOTAL RECALL v2 Medium MASTER CODER at Q4_K_M (25.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.