Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled — Hardware Requirements & GPU Compatibility
ChatReasoning
Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled is a 752M-parameter open language model from Ishant06 in the Qwen 3.5 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 0.80 GB of VRAM — see which GPUs and Macs can run it below.
2 likesSpecifications
- Publisher
- Ishant06
- Family
- Qwen 3.5
- Parameters
- 752M
- Architecture
- Qwen3_5ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-15
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.7 GB | 7.1 GB | 0.32 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.7 GB | 7.1 GB | 0.37 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.8 GB | 7.2 GB | 0.45 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.9 GB | 7.3 GB | 0.54 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.0 GB | 7.4 GB | 0.62 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.1 GB | 7.5 GB | 0.75 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1.9 GB | 8.3 GB | 1.50 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 Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
Q4_K_M · 0.8 GBQwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled (Q4_K_M) requires 0.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 6.4 GB, bringing total usage to 7.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
Q4_K_M · 0.8 GB59 devices with unified memory can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled need?
Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled requires 0.8 GB of VRAM at Q4_K_M, or 1.9 GB at BF16. Full 262K context adds up to 6.4 GB (7.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 752M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M0.8 GBQ4_K_M + full context7.2 GB- What's the best quantization for Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
For Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled, Q4_K_M (0.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.7 GB.
VRAM requirement by quantization
Q2_K0.7 GBQ4_K_M ★0.8 GBQ5_K_M0.9 GBQ6_K1.0 GBQ8_01.1 GBBF161.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled on a Mac?
Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled requires at least 0.7 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 Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled locally?
Yes — Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled can run locally on consumer hardware. At Q4_K_M quantization it needs 0.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
At Q4_K_M, Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled can reach ~6000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~819 tok/s. 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 ÷ 0.8 × 0.65 = ~6500 tok/s
Estimated speed at Q4_K_M (0.8 GB)
~6500 tok/s~819 tok/s~6500 tok/s~6000 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
At Q4_K_M, the download is about 0.45 GB. The full-precision BF16 version is 1.50 GB. The smallest option (Q2_K) is 0.32 GB.
- Which GPUs can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
52 consumer GPUs can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (0.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled?
59 devices with unified memory can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (0.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.