Sarashina2.2 0.5B Instruct v0.1 — Hardware Requirements & GPU Compatibility
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Sarashina2.2 0.5B Instruct v0.1 is a 793M-parameter open language model from sbintuitions. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 0.90 GB of VRAM — see which GPUs and Macs can run it below.
227.7K downloads
16 likesSpecifications
- Publisher
- sbintuitions
- Parameters
- 793M
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 102,400
- Release Date
- 2025-02-26
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Sarashina2.2 0.5B Instruct v0.1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | 1.1 GB | 0.34 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.8 GB | 1.1 GB | 0.35 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 1.2 GB | 0.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.8 GB | 1.2 GB | 0.40 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.9 GB | 1.3 GB | 0.48 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.0 GB | 1.4 GB | 0.57 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.1 GB | 1.5 GB | 0.65 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.2 GB | 1.6 GB | 0.79 GB | 8-bit quantization, near-lossless |
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 Sarashina2.2 0.5B Instruct v0.1?
Q4_K_M · 0.9 GBSarashina2.2 0.5B Instruct v0.1 (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 8K context window can add up to 0.4 GB, bringing total usage to 1.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Sarashina2.2 0.5B Instruct v0.1?
Q4_K_M · 0.9 GB59 devices with unified memory can run Sarashina2.2 0.5B Instruct v0.1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Sarashina2.2 0.5B Instruct v0.1
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Sarashina2.2 0.5B Instruct v0.1 need?
Sarashina2.2 0.5B Instruct v0.1 requires 0.9 GB of VRAM at Q4_K_M, or 2.0 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 793M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.8 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context1.3 GB- What's the best quantization for Sarashina2.2 0.5B Instruct v0.1?
For Sarashina2.2 0.5B Instruct v0.1, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (0.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.6 GB.
VRAM requirement by quantization
IQ2_XXS0.6 GBQ2_K0.8 GBQ4_00.8 GBQ4_K_M ★0.9 GBQ5_00.9 GBBF162.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Sarashina2.2 0.5B Instruct v0.1 on a Mac?
Sarashina2.2 0.5B Instruct v0.1 requires at least 0.6 GB at IQ2_XXS, 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 Sarashina2.2 0.5B Instruct v0.1 locally?
Yes — Sarashina2.2 0.5B Instruct v0.1 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 Sarashina2.2 0.5B Instruct v0.1?
At Q4_K_M, Sarashina2.2 0.5B Instruct v0.1 can reach ~5333 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~728 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 = ~5778 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~5778 tok/s~728 tok/s~5778 tok/s~5333 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Sarashina2.2 0.5B Instruct v0.1?
At Q4_K_M, the download is about 0.48 GB. The full-precision BF16 version is 1.59 GB. The smallest option (IQ2_XXS) is 0.22 GB.
- Which GPUs can run Sarashina2.2 0.5B Instruct v0.1?
52 consumer GPUs can run Sarashina2.2 0.5B Instruct v0.1 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 Sarashina2.2 0.5B Instruct v0.1?
59 devices with unified memory can run Sarashina2.2 0.5B Instruct v0.1 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.