Gallium 350M — Hardware Requirements & GPU Compatibility
ChatCodeReasoning
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 125K)Best on 24 GB (Q4_K_M · ~1130 tok/s · 125K)Macs from 8 GB (Q4_K_M · ~124 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gallium 350M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve DireDreadlord/Gallium-350M --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~403 tok/s
- generation speed
- Cost per 1M output tokens
- $0.052
- per 1M output tokens
How Much VRAM Does Gallium 350M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | 0.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 125K context · ~1260 tok/s | |
| 0.5 GB | 0.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 125K context · ~1213 tok/s | |
| 0.6 GB | 1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 125K context · ~1130 tok/s | |
| 0.6 GB | 1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 125K context · ~1057 tok/s | |
| 0.7 GB | 1.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 125K context · ~993 tok/s | |
| 0.7 GB | 1.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 125K context · ~910 tok/s | |
| 1.1 GB | 1.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 125K context · ~612 tok/s |
Selected Q4_K_M
4-bit medium quantization0.8GBat 16K context
- Weights
- 0.2 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.3 GB free · ~1130 tok/s
≥50 tok/s needs ≥45 GB/s · RTX 5090 ~2008, RTX 3090 Ti ~1130
Which Hardware 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 1.8 GB, bringing total usage to 2.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gallium 350M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.3 GB free · up to 125K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~852Simulate output on RTX 3080RTX 2080 Ti~690Simulate output on RTX 2080 TiRTX 3070 Ti~682Simulate output on RTX 3070 TiM3*8 GB~124Simulate output on iMac 24" M3 (8 GB)Orin Nano~114Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~82Simulate output on iPhone 1720 GB
fits · 19.3 GB free · up to 125K ctx
1 GPU
RX 7900 XT~828Simulate output on RX 7900 XT32–40 GB
fits · 31.3 GB free · up to 125K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2008Simulate output on RTX 5090M5 Max36 GB~555Simulate output on Mac Studio M5 Max (36 GB)M532 GB~185Simulate output on MacBook Air 13" M5 (32 GB)Orin~230Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~151Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite 4.0 350MIBM | — | 1 GB | ~1150 | same size |
| SmolLM 360MHugging Face | — | 0.6 GB | ~1092 | same size |
| LFM2.5 350MLiquid AI | — | 0.8 GB | ~1130 | same size |
| ERNIE 4.5 0.3B PTBaidu | — | 0.7 GB | ~1213 | same size |
| LFM2 350MLiquid AI | — | 0.8 GB | ~1130 | same size |
Specifications
- Publisher
- DireDreadlord
- Parameters
- 353M
- Architecture
- Lfm2ForCausalLM
- Context Length
- 128,000 tokens
- Vocabulary Size
- 64,402
- Release Date
- 2026-07-08
- License
- Other
Frequently 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 1.8 GB (2.4 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.2 GB (at full 128K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Gallium 350M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gallium 350M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gallium 350M locally?
Yes — Gallium 350M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gallium 350M?
At Q4_K_M, Gallium 350M can reach ~8276 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 ÷ 0.58 × 0.65 = ~8966 tok/s
Estimated speed at Q4_K_M (0.6 GB)
Real-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?
52 consumer GPUs can run Gallium 350M at Q4_K_M. 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 Gallium 350M?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.