Baguettotron — Hardware Requirements & GPU Compatibility
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Baguettotron is a 321M-parameter open language model from PleIAs. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 0.62 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 · 4K)Best on 24 GB (Q4_K_M · ~1057 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~116 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Baguettotron
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve PleIAs/Baguettotron --max-model-len 4096vLLM 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
- ~377 tok/s
- generation speed
- Cost per 1M output tokens
- $0.061
- per 1M output tokens
How Much VRAM Does Baguettotron Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~1170 tok/s | |
| 0.6 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~1130 tok/s | |
| 0.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~1057 tok/s | |
| 0.7 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~1008 tok/s | |
| 0.7 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~950 tok/s | |
| 0.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~874 tok/s | |
| 1.1 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~612 tok/s |
Selected Q4_K_M
4-bit medium quantization0.8GBat 4K context
- Weights
- 0.2 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.3 GB free · ~1057 tok/s
≥50 tok/s needs ≥48 GB/s · RTX 5090 ~1879, RTX 3090 Ti ~1057
Which Hardware Can Run Baguettotron?
Q4_K_M · 0.6 GBBaguettotron (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 4K context window can add up to 0.1 GB, bringing total usage to 0.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Baguettotron, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.3 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~797Simulate output on RTX 3080RTX 2080 Ti~646Simulate output on RTX 2080 TiRTX 3070 Ti~638Simulate output on RTX 3070 TiM3*8 GB~116Simulate output on iMac 24" M3 (8 GB)Orin Nano~107Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~77Simulate output on iPhone 1720 GB
fits · 19.3 GB free · up to 4K ctx
1 GPU
RX 7900 XT~774Simulate output on RX 7900 XT32–40 GB
fits · 31.3 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1879Simulate output on RTX 5090M5 Max36 GB~519Simulate output on Mac Studio M5 Max (36 GB)M532 GB~173Simulate output on MacBook Air 13" M5 (32 GB)Orin~215Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~142Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 2B DSparkOpenBMB | — | 0.6 GB | ~1310 | same size |
| LFM2.5 2.6B DSparkLiquid AI | — | 0.7 GB | ~1260 | same size |
| T5gemma S S PrefixlmGoogle | — | 1.7 GB | ~950 | same size |
Specifications
- Publisher
- PleIAs
- Parameters
- 321M
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2025-11-10
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Baguettotron need?
Baguettotron requires 0.6 GB of VRAM at Q4_K_M, or 1.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 321M × 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 ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Baguettotron?
For Baguettotron, Q4_K_M (0.6 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Baguettotron on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Baguettotron. 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 Baguettotron locally?
Yes — Baguettotron can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Baguettotron?
At Q4_K_M, Baguettotron can reach ~7742 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1057 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 ÷ 0.62 × 0.65 = ~8387 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 Baguettotron?
At Q4_K_M, the download is about 0.19 GB. The full-precision BF16 version is 0.64 GB. The smallest option (Q2_K) is 0.14 GB.
Which GPUs can run Baguettotron?
52 consumer GPUs can run Baguettotron 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 Baguettotron?
163 devices with unified memory can run Baguettotron 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.