OmniCoder 9B — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
OmniCoder 9B is a 9.4B-parameter open language model from Tesslate. It supports a context window of up to 262,144 tokens. At BF16 it needs about 19.39 GB of VRAM — see which GPUs and Macs can run it below.
744 downloads
683 likesSpecifications
- Publisher
- Tesslate
- Parameters
- 9.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OmniCoder 9B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 19.4 GB | 53.5 GB | 18.82 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 OmniCoder 9B?
BF16 · 19.4 GBOmniCoder 9B (BF16) requires 19.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 53.5 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 OmniCoder 9B?
BF16 · 19.4 GB41 devices with unified memory can run OmniCoder 9B, 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 OmniCoder 9B need?
OmniCoder 9B requires 19.4 GB of VRAM at BF16. Full 262K context adds up to 34.1 GB (53.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.4B × 16 bits ÷ 8 = 18.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
BF1619.4 GBBF16 + full context53.5 GB- Can I run OmniCoder 9B on a Mac?
OmniCoder 9B requires at least 19.4 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 OmniCoder 9B locally?
Yes — OmniCoder 9B can run locally on consumer hardware. At BF16 quantization it needs 19.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OmniCoder 9B?
At BF16, OmniCoder 9B can reach ~248 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~34 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 ÷ 19.4 × 0.65 = ~268 tok/s
Estimated speed at BF16 (19.4 GB)
~268 tok/s~34 tok/s~268 tok/s~248 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OmniCoder 9B?
At BF16, the download is about 18.82 GB.
- Which GPUs can run OmniCoder 9B?
8 consumer GPUs can run OmniCoder 9B at BF16 (19.4 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 OmniCoder 9B?
41 devices with unified memory can run OmniCoder 9B at BF16 (19.4 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.