Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (19.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.

How fast is Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled can reach ~167 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~38 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

At Q4_K_M, the download is about 16.67 GB. The full-precision Q8_0 version is 27.78 GB. The smallest option (IQ2_XXS) is 7.64 GB.

Jackrong·Qwen·Qwen3_5ForConditionalGeneration

Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled — Hardware Requirements & GPU Compatibility

ChatReasoning

The full-precision version of Jackrong's Qwen3.5 27B reasoning distillation from Claude 4.6 Opus. With 27.8 billion parameters in unquantized form, this model preserves the maximum quality from the distillation process but requires significantly more VRAM, typically 56 GB or more in BF16. It is primarily intended for users with professional-grade GPUs or multi-GPU setups. This variant is ideal for further fine-tuning, experimentation, or running at full fidelity when hardware allows. Most users looking to run the model locally for inference should consider the GGUF-quantized version instead, which offers a much better tradeoff between quality and resource usage.

61.6K downloads 695 likesMar 2026262K context

Specifications

Publisher: Jackrong
Family: Qwen
Parameters: 27.8B
Architecture: Qwen3_5ForConditionalGeneration
Context Length: 262,144 tokens
Vocabulary Size: 248,320
Release Date: 2026-03-07
License: Apache 2.0

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HuggingFace

Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

How Much VRAM Does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	8.4 GB	65.2 GB	7.64 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_XS	2.40	9.1 GB	65.9 GB	8.33 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	9.4 GB	66.3 GB	8.68 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	10.1 GB	66.9 GB	9.38 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	11.5 GB	68.3 GB	10.77 GB	Importance-weighted 3-bit
Q2_K_S	3.20	11.9 GB	68.7 GB	11.11 GB	2-bit small K-quant
IQ3_XS	3.30	12.2 GB	69.0 GB	11.46 GB	Importance-weighted 3-bit, extra small
Q2_K	3.40	12.6 GB	69.4 GB	11.81 GB	2-bit quantization with K-quant improvements
IQ3_S	3.40	12.6 GB	69.4 GB	11.81 GB	Importance-weighted 3-bit, small
Q3_K_S	3.50	12.9 GB	69.7 GB	12.15 GB	3-bit small quantization
IQ3_M	3.60	13.3 GB	70.1 GB	12.50 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	14.3 GB	71.1 GB	13.54 GB	3-bit medium quantization
Q4_0	4.00	14.6 GB	71.5 GB	13.89 GB	4-bit legacy quantization
Q3_K_L	4.10	15.0 GB	71.8 GB	14.24 GB	3-bit large quantization
IQ4_XS	4.30	15.7 GB	72.5 GB	14.93 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	16.4 GB	73.2 GB	15.63 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	16.4 GB	73.2 GB	15.63 GB	4-bit small quantization
Q4_K_M	4.80	17.4 GB	74.2 GB	16.67 GB	4-bit medium quantization — most popular sweet spot
Q5_K_S	5.50	19.9 GB	76.7 GB	19.10 GB	5-bit small quantization
Q5_K_M	5.70	20.5 GB	77.4 GB	19.79 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	23.7 GB	80.5 GB	22.92 GB	6-bit quantization, very good quality
Q8_0	8.00	28.5 GB	85.3 GB	27.78 GB	8-bit quantization, near-lossless

Which GPUs Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

Q4_K_M · 17.4 GB

Show professional

Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled (Q4_K_M) requires 17.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 74.2 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~67 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~38 tok/s NVIDIA GeForce RTX 4090~38 tok/s NVIDIA GeForce RTX 3090~35 tok/s AMD Radeon RX 7900 XTX~30 tok/s AMD Radeon RX 7900 XT~25 tok/s

Which Devices Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?

Q4_K_M · 17.4 GB

21 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Derivatives (8)

MLX Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled 4bit Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled FP8 Dynamic Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic V1 Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Heretic v2 Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled I1 GGUF Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX

Frequently Asked Questions

How much VRAM does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled need?: Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled requires 17.4 GB of VRAM at Q4_K_M, or 28.5 GB at Q8_0. Full 262K context adds up to 56.8 GB (74.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M
17.4 GB
Q4_K_M + full context
74.2 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?: Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?: For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (19.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.
VRAM requirement by quantization
IQ2_XXS
8.4 GB~53%
Q2_K_S
11.9 GB~71%
Q3_K_M
14.3 GB~83%
Q4_K_S
16.4 GB~88%
Q4_K_M ★
17.4 GB~89%
Q8_0
28.5 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled on a Mac?: Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled requires at least 8.4 GB at IQ2_XXS, 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 Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled locally?: Yes — Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled can run locally on consumer hardware. At Q4_K_M quantization it needs 17.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?: At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled can reach ~167 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~38 tok/s. 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 ÷ 17.4 × 0.55 = ~167 tok/s
Estimated speed at Q4_K_M (17.4 GB)
AMD Instinct MI300X
~167 tok/s
NVIDIA GeForce RTX 4090
~38 tok/s
NVIDIA H100 SXM
~125 tok/s
AMD Instinct MI250X
~104 tok/s
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
Learn more about tok/s estimation →
What's the download size of Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?: At Q4_K_M, the download is about 16.67 GB. The full-precision Q8_0 version is 27.78 GB. The smallest option (IQ2_XXS) is 7.64 GB.