Quasar 10B — Hardware Requirements & GPU Compatibility
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Quasar 10B is a 8.6B-parameter open language model from silx-ai. It supports a context window of up to 2,097,152 tokens. At BF16 it needs about 17.77 GB of VRAM — see which GPUs and Macs can run it below.
4.9K downloads
49 likesSpecifications
- Publisher
- silx-ai
- Parameters
- 8.6B
- Architecture
- QuasarForCausalLM
- Context Length
- 2,097,152 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-09
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Quasar 10B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17.8 GB | 292.4 GB | 17.20 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 Quasar 10B?
BF16 · 17.8 GBQuasar 10B (BF16) requires 17.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 2097K context window can add up to 274.6 GB, bringing total usage to 292.4 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 Quasar 10B?
BF16 · 17.8 GB41 devices with unified memory can run Quasar 10B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does Quasar 10B need?
Quasar 10B requires 17.8 GB of VRAM at BF16. Full 2097K context adds up to 274.6 GB (292.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.6B × 16 bits ÷ 8 = 17.2 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 275.2 GB (at full 2097K context)
VRAM usage by quantization
BF1617.8 GBBF16 + full context292.4 GB- Can I run Quasar 10B on a Mac?
Quasar 10B requires at least 17.8 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 Quasar 10B locally?
Yes — Quasar 10B can run locally on consumer hardware. At BF16 quantization it needs 17.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Quasar 10B?
At BF16, Quasar 10B can reach ~248 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.8 × 0.65 = ~293 tok/s
Estimated speed at BF16 (17.8 GB)
~293 tok/s~37 tok/s~293 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 Quasar 10B?
At BF16, the download is about 17.20 GB.
- Which GPUs can run Quasar 10B?
8 consumer GPUs can run Quasar 10B at BF16 (17.8 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 Quasar 10B?
41 devices with unified memory can run Quasar 10B at BF16 (17.8 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.