Qwen3 VL 8B Heretic 1.3.0 — Hardware Requirements & GPU Compatibility
ChatVision
Qwen3 VL 8B Heretic 1.3.0 is a 8.8B-parameter open language model from DreamFast in the Qwen 3 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 5.86 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 · 16K)Best on 24 GB (Q4_K_M · ~112 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~24 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 VL 8B Heretic 1.3.0
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 DreamFast/Qwen3-VL-8B-Heretic-1.3.0 --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) (4 GB headroom) · Q4_K_M
- Generation speed
- ~101 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does Qwen3 VL 8B Heretic 1.3.0 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | 8.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~151 tok/s | |
| 4.9 GB | 9.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~134 tok/s | |
| 5.5 GB | 10.1 GB | 4-bit small — 4-bit small quantization | Fits: 64K context · ~119 tok/s | |
| 5.9 GB | 10.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~112 tok/s | |
| 6.6 GB | 11.2 GB | 5-bit small — 5-bit small quantization | Fits: 64K context · ~99 tok/s | |
| 6.8 GB | 11.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~96 tok/s | |
| 7.8 GB | 12.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~84 tok/s | |
| 9.4 GB | 13.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~70 tok/s | |
| 18.1 GB | 22.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~36 tok/s |
Selected Q4_K_M
4-bit medium quantization8.0GBat 16K context
- Weights
- 5.3 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.0 GB free · ~112 tok/s
≥50 tok/s needs ≥450 GB/s · RTX 5090 ~199, RTX 3090 Ti ~112
Which Hardware Can Run Qwen3 VL 8B Heretic 1.3.0?
Q4_K_M · 5.9 GBQwen3 VL 8B Heretic 1.3.0 (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 262K context window can add up to 38.4 GB, bringing total usage to 44.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Qwen3 VL 8B Heretic 1.3.0, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.0 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~84Simulate output on RTX 3080RTX 2080 Ti~68Simulate output on RTX 2080 TiRTX 3070 Ti~68Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.0 GB free · up to 64K ctx
1 GPU
RX 7900 XT~82Simulate output on RX 7900 XT32–40 GB
fits · 24.0 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~199Simulate output on RTX 5090M5 Max36 GB~55Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 VL 8B Heretic 1.3.0
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| Ornith 1.0 9BDeep Reinforce | — | 6.5 GB | ~106 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
Specifications
- Publisher
- DreamFast
- Family
- Qwen 3
- Parameters
- 8.8B
- Architecture
- Qwen3VLForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-06-05
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 VL 8B Heretic 1.3.0 need?
Qwen3 VL 8B Heretic 1.3.0 requires 5.9 GB of VRAM at Q4_K_M, or 18.1 GB at BF16. Full 262K context adds up to 38.4 GB (44.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 4.8 bits ÷ 8 = 5.3 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 38.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Qwen3 VL 8B Heretic 1.3.0?
For Qwen3 VL 8B Heretic 1.3.0, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 VL 8B Heretic 1.3.0 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3 VL 8B Heretic 1.3.0. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 VL 8B Heretic 1.3.0 locally?
Yes — Qwen3 VL 8B Heretic 1.3.0 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 VL 8B Heretic 1.3.0?
At Q4_K_M, Qwen3 VL 8B Heretic 1.3.0 can reach ~819 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~112 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 ÷ 5.86 × 0.65 = ~887 tok/s
Estimated speed at Q4_K_M (5.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 VL 8B Heretic 1.3.0?
At Q4_K_M, the download is about 5.26 GB. The full-precision BF16 version is 17.53 GB. The smallest option (Q2_K) is 3.73 GB.
Which GPUs can run Qwen3 VL 8B Heretic 1.3.0?
52 consumer GPUs can run Qwen3 VL 8B Heretic 1.3.0 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen3 VL 8B Heretic 1.3.0?
151 devices with unified memory can run Qwen3 VL 8B Heretic 1.3.0 at Q4_K_M (5.9 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.