Gallium 350M — Hardware Requirements & GPU Compatibility
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Gallium 350M is a 353M-parameter open language model from DireDreadlord. It supports a context window of up to 128,000 tokens. At Q4_K_M it needs about 0.58 GB of VRAM — see which GPUs and Macs can run it below.
273 downloads
9 likesSpecifications
- Publisher
- DireDreadlord
- Parameters
- 353M
- Architecture
- Lfm2ForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 64,402
- Release Date
- 2026-07-08
- License
- Other
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HuggingFace
How Much VRAM Does Gallium 350M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.5 GB | 4.6 GB | 0.15 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.5 GB | 4.7 GB | 0.17 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.6 GB | 4.7 GB | 0.21 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.6 GB | 4.8 GB | 0.25 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 0.7 GB | 4.8 GB | 0.29 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 0.7 GB | 4.8 GB | 0.35 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1.1 GB | 5.2 GB | 0.71 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 Gallium 350M?
Q4_K_M · 0.6 GBGallium 350M (Q4_K_M) requires 0.6 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 128K context window can add up to 4.1 GB, bringing total usage to 4.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gallium 350M?
Q4_K_M · 0.6 GB59 devices with unified memory can run Gallium 350M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Gallium 350M need?
Gallium 350M requires 0.6 GB of VRAM at Q4_K_M, or 1.1 GB at BF16. Full 128K context adds up to 4.1 GB (4.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 353M × 4.8 bits ÷ 8 = 0.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M0.6 GBQ4_K_M + full context4.7 GB- What's the best quantization for Gallium 350M?
For Gallium 350M, Q4_K_M (0.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
Q2_K0.5 GBQ4_K_M ★0.6 GBQ5_K_M0.6 GBQ6_K0.7 GBQ8_00.7 GBBF161.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gallium 350M on a Mac?
Gallium 350M requires at least 0.5 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 Gallium 350M locally?
Yes — Gallium 350M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gallium 350M?
At Q4_K_M, Gallium 350M can reach ~7586 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1130 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 ÷ 0.6 × 0.65 = ~8966 tok/s
Estimated speed at Q4_K_M (0.6 GB)
~8966 tok/s~1130 tok/s~8966 tok/s~7586 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gallium 350M?
At Q4_K_M, the download is about 0.21 GB. The full-precision BF16 version is 0.71 GB. The smallest option (Q2_K) is 0.15 GB.
- Which GPUs can run Gallium 350M?
50 consumer GPUs can run Gallium 350M at Q4_K_M (0.6 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 Gallium 350M?
59 devices with unified memory can run Gallium 350M at Q4_K_M (0.6 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.