CRia LM 75M Instruct — Hardware Requirements & GPU Compatibility
Chat
CRia LM 75M Instruct is a 76M-parameter open language model from sz14. It supports a context window of up to 4,096 tokens. At BF16 it needs about 0.47 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
3 likesSpecifications
- Publisher
- sz14
- Parameters
- 76M
- Architecture
- RRTForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2026-09-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does CRia LM 75M Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.5 GB | 0.5 GB | 0.15 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 CRia LM 75M Instruct?
BF16 · 0.5 GBCRia LM 75M Instruct (BF16) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run CRia LM 75M Instruct?
BF16 · 0.5 GB59 devices with unified memory can run CRia LM 75M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does CRia LM 75M Instruct need?
CRia LM 75M Instruct requires 0.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 76M × 16 bits ÷ 8 = 0.2 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.3 GB (at full 4K context)
VRAM usage by quantization
BF160.5 GBBF16 + full context0.5 GB- Can I run CRia LM 75M Instruct on a Mac?
CRia LM 75M Instruct requires at least 0.5 GB at BF16, 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 CRia LM 75M Instruct locally?
Yes — CRia LM 75M Instruct can run locally on consumer hardware. At BF16 quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CRia LM 75M Instruct?
At BF16, CRia LM 75M Instruct can reach ~10213 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1394 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 ÷ 0.5 × 0.65 = ~11064 tok/s
Estimated speed at BF16 (0.5 GB)
~11064 tok/s~1394 tok/s~11064 tok/s~10213 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of CRia LM 75M Instruct?
At BF16, the download is about 0.15 GB.
- Which GPUs can run CRia LM 75M Instruct?
52 consumer GPUs can run CRia LM 75M Instruct at BF16 (0.5 GB). 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 CRia LM 75M Instruct?
59 devices with unified memory can run CRia LM 75M Instruct at BF16 (0.5 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.