Math Instruct V1 — Hardware Requirements & GPU Compatibility
ChatMath
Math Instruct V1 is a 596M-parameter open language model from kaushik-harsh-99. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 0.78 GB of VRAM — see which GPUs and Macs can run it below.
64 downloads
3 likesSpecifications
- Publisher
- kaushik-harsh-99
- Parameters
- 596M
- Architecture
- Qwen3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-06-07
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Math Instruct V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.7 GB | 2.4 GB | 0.25 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.7 GB | 2.5 GB | 0.29 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.8 GB | 2.5 GB | 0.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.8 GB | 2.6 GB | 0.42 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.9 GB | 2.7 GB | 0.49 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.0 GB | 2.8 GB | 0.60 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1.6 GB | 3.4 GB | 1.19 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 Math Instruct V1?
Q4_K_M · 0.8 GBMath Instruct V1 (Q4_K_M) requires 0.8 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 33K context window can add up to 1.8 GB, bringing total usage to 2.5 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Math Instruct V1?
Q4_K_M · 0.8 GB59 devices with unified memory can run Math Instruct V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Math Instruct V1
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 Math Instruct V1 need?
Math Instruct V1 requires 0.8 GB of VRAM at Q4_K_M, or 1.6 GB at BF16. Full 33K context adds up to 1.8 GB (2.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 596M × 4.8 bits ÷ 8 = 0.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M0.8 GBQ4_K_M + full context2.5 GB- What's the best quantization for Math Instruct V1?
For Math Instruct V1, Q4_K_M (0.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 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 ★0.8 GBQ5_K_M0.8 GBQ6_K0.9 GBQ8_01.0 GBBF161.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Math Instruct V1 on a Mac?
Math Instruct V1 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 Math Instruct V1 locally?
Yes — Math Instruct V1 can run locally on consumer hardware. At Q4_K_M quantization it needs 0.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Math Instruct V1?
At Q4_K_M, Math Instruct V1 can reach ~5641 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~840 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 ÷ 0.8 × 0.65 = ~6667 tok/s
Estimated speed at Q4_K_M (0.8 GB)
~6667 tok/s~840 tok/s~6667 tok/s~5641 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Math Instruct V1?
At Q4_K_M, the download is about 0.36 GB. The full-precision BF16 version is 1.19 GB. The smallest option (Q2_K) is 0.25 GB.
- Which GPUs can run Math Instruct V1?
50 consumer GPUs can run Math Instruct V1 at Q4_K_M (0.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Math Instruct V1?
59 devices with unified memory can run Math Instruct V1 at Q4_K_M (0.8 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.