Qwen3.5 0.8B — Hardware Requirements & GPU Compatibility
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Qwen3.5 0.8B is a 0.8B-parameter open language model from titan0115 in the Qwen 3.5 family. At Q4_K_M it needs about 0.53 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 · 32K)Best on 24 GB (Q4_K_M · ~1236 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~135 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 0.8B
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 titan0115/Qwen3.5-0.8B --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) (10 GB headroom) · Q4_K_M
- Generation speed
- ~1119 tok/s
- generation speed
- Cost per 1M output tokens
- $0.030
- per 1M output tokens
How Much VRAM Does Qwen3.5 0.8B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | 3.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~1771 tok/s | |
| 0.4 GB | 3.3 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~1680 tok/s | |
| 0.4 GB | 3.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~1524 tok/s | |
| 0.4 GB | 3.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~1489 tok/s | |
| 0.5 GB | 3.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~1236 tok/s | |
| 0.6 GB | 3.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~1040 tok/s | |
| 0.7 GB | 3.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~898 tok/s | |
| 0.9 GB | 3.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~745 tok/s |
Selected Q4_K_M
4-bit medium quantization1.9GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.1 GB free · ~1236 tok/s
≥50 tok/s needs ≥41 GB/s · RTX 5090 ~2198, RTX 3090 Ti ~1236
Which Hardware Can Run Qwen3.5 0.8B?
Q4_K_M · 0.5 GBQwen3.5 0.8B (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 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, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.1 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~932Simulate output on RTX 3080RTX 2080 Ti~756Simulate output on RTX 2080 TiRTX 3070 Ti~746Simulate output on RTX 3070 TiM3*8 GB~135Simulate output on iMac 24" M3 (8 GB)Orin Nano~125Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~90Simulate output on iPhone 1720 GB
fits · 18.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~906Simulate output on RX 7900 XT32–40 GB
fits · 30.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2198Simulate output on RTX 5090M5 Max36 GB~608Simulate output on Mac Studio M5 Max (36 GB)M532 GB~203Simulate output on MacBook Air 13" M5 (32 GB)Orin~251Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~166Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.5 0.8B
Qwen 3.5 family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 0.6BAlibaba | — | 1.7 GB | ~753 | same size |
| Bitnet B1.58 2B 4TMicrosoft | — | 2.3 GB | ~303 | 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 |
| H2ovl Mississippi 800Mh2oai | — | 3.2 GB | ~360 | same size |
Specifications
- Publisher
- titan0115
- Family
- Qwen 3.5
- Parameters
- 0.8B
- Release Date
- 2026-03-02
Frequently Asked Questions
How much VRAM does Qwen3.5 0.8B need?
Qwen3.5 0.8B requires 0.5 GB of VRAM at Q4_K_M, or 1.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 0.8B × 4.8 bits ÷ 8 = 0.5 GB
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Qwen3.5 0.8B?
For Qwen3.5 0.8B, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.5 0.8B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3.5 0.8B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.5 0.8B locally?
Yes — Qwen3.5 0.8B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 0.8B?
At Q4_K_M, Qwen3.5 0.8B can reach ~9057 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1236 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 ÷ 0.53 × 0.65 = ~9811 tok/s
Estimated speed at Q4_K_M (0.5 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?
At Q4_K_M, the download is about 0.48 GB. The full-precision BF16 version is 1.60 GB. The smallest option (IQ2_M) is 0.27 GB.
Which GPUs can run Qwen3.5 0.8B?
52 consumer GPUs can run Qwen3.5 0.8B 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?
163 devices with unified memory can run Qwen3.5 0.8B at Q4_K_M (0.5 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.