Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled — Hardware Requirements & GPU Compatibility
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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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 256K)Best on 24 GB (Q4_K_M · ~819 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~90 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve Ishant06/Qwen3.5-0.8B-Claude-4.6-Opus-Reasoning-Distilled --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~741 tok/s
- generation speed
- Cost per 1M output tokens
- $0.046
- per 1M output tokens
How Much VRAM Does Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.7 GB | 0.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~978 tok/s | |
| 0.7 GB | 0.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~910 tok/s | |
| 0.8 GB | 1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~819 tok/s | |
| 0.9 GB | 1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~736 tok/s | |
| 1.0 GB | 1.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~676 tok/s | |
| 1.1 GB | 1.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~596 tok/s | |
| 1.9 GB | 2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~352 tok/s |
Selected Q4_K_M
4-bit medium quantization0.9GBat 16K context
- Weights
- 0.5 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.2 GB free · ~819 tok/s
≥50 tok/s needs ≥61 GB/s · RTX 5090 ~1456, RTX 3090 Ti ~819
Which Hardware 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 1.6 GB, bringing total usage to 2.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.2 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~618Simulate output on RTX 3080RTX 2080 Ti~501Simulate output on RTX 2080 TiRTX 3070 Ti~494Simulate output on RTX 3070 TiM3*8 GB~90Simulate output on iMac 24" M3 (8 GB)Orin Nano~83Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~60Simulate output on iPhone 1720 GB
fits · 19.1 GB free · up to 256K ctx
1 GPU
RX 7900 XT~600Simulate output on RX 7900 XT32–40 GB
fits · 31.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1456Simulate output on RTX 5090M5 Max36 GB~403Simulate output on Mac Studio M5 Max (36 GB)M532 GB~134Simulate output on MacBook Air 13" M5 (32 GB)Orin~166Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~110Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled
Qwen 3.5 family · 34 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 0.6BAlibaba | — | 1.7 GB | ~753 | same size |
| Sarashina2.2 0.5B Instruct v0.1sbintuitions | — | 1.3 GB | ~728 | same size |
| Gpt2 LargeOpenAI | — | 0.5 GB | ~1213 | same size |
| OneReason 0.8B Pretrain CompetitionOpenOneRec | — | 1.7 GB | ~728 | same size |
Specifications
- 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
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 1.6 GB (2.4 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.9 GB (at full 262K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3.5 0.8B Claude 4.6 Opus Reasoning Distilled. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.8 GB of usable unified memory (RAM minus macOS overhead).
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. 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)
Real-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. 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?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.