LocoTrainer 4B — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
1.2K downloads
163 likesSpecifications
- Publisher
- LocoreMind
- Parameters
- 4.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-03-14
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does LocoTrainer 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_M | 3.90 | 2.5 GB | 26.4 GB | 1.96 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 2.9 GB | 26.9 GB | 2.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | 27.3 GB | 2.87 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q8_0 | 8.00 | 4.5 GB | 28.5 GB | 4.02 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run LocoTrainer 4B?
Q4_K_M · 2.9 GBLocoTrainer 4B (Q4_K_M) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 26.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run LocoTrainer 4B?
Q4_K_M · 2.9 GB33 devices with unified memory can run LocoTrainer 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does LocoTrainer 4B need?
LocoTrainer 4B requires 2.9 GB of VRAM at Q4_K_M, or 4.5 GB at Q8_0. Full 262K context adds up to 24.0 GB (26.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 24.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M2.9 GBQ4_K_M + full context26.9 GB- What's the best quantization for LocoTrainer 4B?
For LocoTrainer 4B, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.4 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_M at 2.5 GB.
VRAM requirement by quantization
Q3_K_M2.5 GB~83%Q4_K_M ★2.9 GB~89%Q5_K_M3.4 GB~92%Q8_04.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run LocoTrainer 4B on a Mac?
LocoTrainer 4B requires at least 2.5 GB at Q3_K_M, 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 LocoTrainer 4B locally?
Yes — LocoTrainer 4B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LocoTrainer 4B?
At Q4_K_M, LocoTrainer 4B can reach ~1005 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~226 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.9 × 0.55 = ~1005 tok/s
Estimated speed at Q4_K_M (2.9 GB)
AMD Instinct MI300X~1005 tok/sNVIDIA GeForce RTX 4090~226 tok/sNVIDIA H100 SXM~751 tok/sAMD Instinct MI250X~622 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LocoTrainer 4B?
At Q4_K_M, the download is about 2.41 GB. The full-precision Q8_0 version is 4.02 GB. The smallest option (Q3_K_M) is 1.96 GB.
- Which GPUs can run LocoTrainer 4B?
35 consumer GPUs can run LocoTrainer 4B at Q4_K_M (2.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LocoTrainer 4B?
33 devices with unified memory can run LocoTrainer 4B at Q4_K_M (2.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.