Stable DiffCoder 8B Instruct — Hardware Requirements & GPU Compatibility
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Stable DiffCoder 8B Instruct is a 8.3B-parameter open language model from ByteDance-Seed. It supports a context window of up to 8,192 tokens. At BF16 it needs about 17.07 GB of VRAM — see which GPUs and Macs can run it below.
691 downloads
126 likesSpecifications
- Publisher
- ByteDance-Seed
- Parameters
- 8.3B
- Architecture
- StableDiffcoderForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 155,136
- Release Date
- 2026-01-15
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Stable DiffCoder 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17.1 GB | 17.9 GB | 16.50 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 Stable DiffCoder 8B Instruct?
BF16 · 17.1 GBStable DiffCoder 8B Instruct (BF16) requires 17.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 17.9 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Stable DiffCoder 8B Instruct?
BF16 · 17.1 GB41 devices with unified memory can run Stable DiffCoder 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Stable DiffCoder 8B Instruct need?
Stable DiffCoder 8B Instruct requires 17.1 GB of VRAM at BF16. Full 8K context adds up to 0.8 GB (17.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 16 bits ÷ 8 = 16.5 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
BF1617.1 GBBF16 + full context17.9 GB- Can I run Stable DiffCoder 8B Instruct on a Mac?
Stable DiffCoder 8B Instruct requires at least 17.1 GB at BF16, 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 Stable DiffCoder 8B Instruct locally?
Yes — Stable DiffCoder 8B Instruct can run locally on consumer hardware. At BF16 quantization it needs 17.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Stable DiffCoder 8B Instruct?
At BF16, Stable DiffCoder 8B Instruct can reach ~258 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.1 × 0.65 = ~305 tok/s
Estimated speed at BF16 (17.1 GB)
~305 tok/s~38 tok/s~305 tok/s~258 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Stable DiffCoder 8B Instruct?
At BF16, the download is about 16.50 GB.
- Which GPUs can run Stable DiffCoder 8B Instruct?
8 consumer GPUs can run Stable DiffCoder 8B Instruct at BF16 (17.1 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Stable DiffCoder 8B Instruct?
41 devices with unified memory can run Stable DiffCoder 8B Instruct at BF16 (17.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.