Intern Decision 0.8B — Hardware Requirements & GPU Compatibility
Vision
Intern-Decision-0.8B is a 0.8-billion-parameter multimodal structured decision model from InternLM, fine-tuned from Qwen3.5-0.8B. Rather than generating free text, it takes a shared state, a schema of named questions and optional images, and returns an answer distribution for every question in a single forward pass, by reading logits over candidate option symbols. It suits classification and typed-decision tasks, and the card reports it scoring 94.52 on ToolACE and 77.35 on Typed Decision. It accepts up to eight images per request. At this size it runs on any consumer GPU or CPU. The context length is 262,144 tokens, with image tokens counting toward the input limit. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2026. It is a specialized scorer built on a Qwen base and is not a general chat model.
1.2K downloads
26 likesSpecifications
- Publisher
- InternLM
- Parameters
- 853M
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Intern Decision 0.8B 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 | 0.36 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.8 GB | 7.2 GB | 0.42 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.9 GB | 7.3 GB | 0.51 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.0 GB | 7.3 GB | 0.61 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.1 GB | 7.5 GB | 0.70 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.2 GB | 7.6 GB | 0.85 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 2.1 GB | 8.4 GB | 1.71 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 Intern Decision 0.8B?
Q4_K_M · 0.9 GBIntern Decision 0.8B (Q4_K_M) requires 0.9 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 7.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Intern Decision 0.8B?
Q4_K_M · 0.9 GB59 devices with unified memory can run Intern Decision 0.8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Intern Decision 0.8B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Intern Decision 0.8B need?
Intern Decision 0.8B requires 0.9 GB of VRAM at Q4_K_M, or 2.1 GB at BF16. Full 262K context adds up to 6.4 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 853M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context7.3 GB- What's the best quantization for Intern Decision 0.8B?
For Intern Decision 0.8B, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.0 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 ★0.9 GBQ5_K_M1.0 GBQ6_K1.1 GBQ8_01.2 GBBF162.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Intern Decision 0.8B on a Mac?
Intern Decision 0.8B 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 Intern Decision 0.8B locally?
Yes — Intern Decision 0.8B can run locally on consumer hardware. At Q4_K_M quantization it needs 0.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Intern Decision 0.8B?
At Q4_K_M, Intern Decision 0.8B can reach ~5581 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~762 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 ÷ 0.9 × 0.65 = ~6047 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~6047 tok/s~762 tok/s~6047 tok/s~5581 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Intern Decision 0.8B?
At Q4_K_M, the download is about 0.51 GB. The full-precision BF16 version is 1.71 GB. The smallest option (Q2_K) is 0.36 GB.
- Which GPUs can run Intern Decision 0.8B?
52 consumer GPUs can run Intern Decision 0.8B at Q4_K_M (0.9 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 Intern Decision 0.8B?
59 devices with unified memory can run Intern Decision 0.8B at Q4_K_M (0.9 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.