Fluxion 370M Instruct — Hardware Requirements & GPU Compatibility
ChatCodeMath
Fluxion 370M Instruct is a 430M-parameter open language model from nareshmeena12. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 0.67 GB of VRAM — see which GPUs and Macs can run it below.
1.5K downloads
5 likesSpecifications
- Publisher
- nareshmeena12
- Parameters
- 430M
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 50,000
- Release Date
- 2026-07-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Fluxion 370M Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.6 GB | 0.7 GB | 0.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.6 GB | 0.7 GB | 0.21 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.7 GB | 0.8 GB | 0.26 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.7 GB | 0.8 GB | 0.31 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.8 GB | 0.9 GB | 0.35 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.8 GB | 0.9 GB | 0.43 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1.3 GB | 1.4 GB | 0.86 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 Fluxion 370M Instruct?
Q4_K_M · 0.7 GBFluxion 370M Instruct (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 0.8 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Fluxion 370M Instruct?
Q4_K_M · 0.7 GB59 devices with unified memory can run Fluxion 370M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Fluxion 370M Instruct need?
Fluxion 370M Instruct requires 0.7 GB of VRAM at Q4_K_M, or 1.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 430M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M0.7 GBQ4_K_M + full context0.8 GB- What's the best quantization for Fluxion 370M Instruct?
For Fluxion 370M Instruct, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.6 GB.
VRAM requirement by quantization
Q2_K0.6 GBQ4_K_M ★0.7 GBQ5_K_M0.7 GBQ6_K0.8 GBQ8_00.8 GBBF161.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Fluxion 370M Instruct on a Mac?
Fluxion 370M Instruct requires at least 0.6 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 Fluxion 370M Instruct locally?
Yes — Fluxion 370M Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Fluxion 370M Instruct?
At Q4_K_M, Fluxion 370M Instruct can reach ~6567 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~978 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 ÷ 0.7 × 0.65 = ~7761 tok/s
Estimated speed at Q4_K_M (0.7 GB)
~7761 tok/s~978 tok/s~7761 tok/s~6567 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Fluxion 370M Instruct?
At Q4_K_M, the download is about 0.26 GB. The full-precision BF16 version is 0.86 GB. The smallest option (Q2_K) is 0.18 GB.
- Which GPUs can run Fluxion 370M Instruct?
50 consumer GPUs can run Fluxion 370M Instruct at Q4_K_M (0.7 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 Fluxion 370M Instruct?
59 devices with unified memory can run Fluxion 370M Instruct at Q4_K_M (0.7 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.