Qwen3.5 0.8B L — Hardware Requirements & GPU Compatibility
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Qwen3.5 0.8B L is a 776M-parameter open language model from trymirai in the Qwen 3.5 family. At BF16 it needs about 1.71 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 32K)Best on 24 GB (BF16 · ~383 tok/s · 32K)Macs from 8 GB (BF16 · ~42 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 0.8B L
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 trymirai/Qwen3.5-0.8B-L --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) (8 GB headroom) · BF16
- Generation speed
- ~347 tok/s
- generation speed
- Cost per 1M output tokens
- $0.098
- per 1M output tokens
How Much VRAM Does Qwen3.5 0.8B L Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.7 GB | 4.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~383 tok/s |
Selected BF16
Brain floating point 163.1GBat 16K context
- Weights
- 1.2 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.0 GB free · ~383 tok/s
≥50 tok/s needs ≥131 GB/s · RTX 5090 ~681, RTX 3090 Ti ~383
Which Hardware Can Run Qwen3.5 0.8B L?
BF16 · 1.7 GBQwen3.5 0.8B L (BF16) requires 1.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ 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 L, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.0 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~289Simulate output on RTX 3080RTX 2080 Ti~234Simulate output on RTX 2080 TiRTX 3070 Ti~231Simulate output on RTX 3070 TiM3*8 GB~42Simulate output on iMac 24" M3 (8 GB)Orin Nano~39Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~28Simulate output on iPhone 1720 GB
fits · 16.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~281Simulate output on RX 7900 XT32–40 GB
fits · 28.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~681Simulate output on RTX 5090M5 Max36 GB~188Simulate output on Mac Studio M5 Max (36 GB)M532 GB~63Simulate output on MacBook Air 13" M5 (32 GB)Orin~78Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~51Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Qwen3.5 0.8B L
Qwen 3.5 family · 34 models →Similar models
5| 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 |
| H2ovl Mississippi 800Mh2oai | — | 3.2 GB | ~360 | same size |
Specifications
- Publisher
- trymirai
- Family
- Qwen 3.5
- Parameters
- 776M
- Release Date
- 2026-06-04
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.5 0.8B L need?
Qwen3.5 0.8B L requires 1.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 776M × 16 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.1 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.
VRAM usage by quantization
Can I run Qwen3.5 0.8B L on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3.5 0.8B L. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 1.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.5 0.8B L locally?
Yes — Qwen3.5 0.8B L can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 0.8B L?
At BF16, Qwen3.5 0.8B L can reach ~2807 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~383 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 ÷ 1.71 × 0.65 = ~3041 tok/s
Estimated speed at BF16 (1.7 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 L?
At BF16, the download is about 1.55 GB.
Which GPUs can run Qwen3.5 0.8B L?
52 consumer GPUs can run Qwen3.5 0.8B L at BF16. 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 L?
163 devices with unified memory can run Qwen3.5 0.8B L at BF16 (1.7 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.