Supra Title 50M Pre — Hardware Requirements & GPU Compatibility
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Supra Title 50M Pre is a 52M-parameter open language model from SupraLabs. It supports a context window of up to 5,120 tokens. At Q4_K_M it needs about 0.36 GB of VRAM — see which GPUs and Macs can run it below.
261 downloads
7 likesSpecifications
- Publisher
- SupraLabs
- Parameters
- 52M
- Architecture
- LlamaForCausalLM
- Context Length
- 5,120 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2026-06-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Supra Title 50M Pre Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_M | 3.90 | 0.3 GB | 0.4 GB | 0.03 GB | 3-bit medium quantization |
| Q2_K | 3.40 | 0.3 GB | 0.4 GB | 0.02 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.3 GB | 0.4 GB | 0.02 GB | 3-bit small quantization |
| Q4_0 | 4.00 | 0.3 GB | 0.4 GB | 0.03 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.4 GB | 0.4 GB | 0.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.4 GB | 0.4 GB | 0.04 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.4 GB | 0.4 GB | 0.04 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.4 GB | 0.4 GB | 0.05 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Supra Title 50M Pre?
Q4_K_M · 0.4 GBSupra Title 50M Pre (Q4_K_M) requires 0.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Supra Title 50M Pre?
Q4_K_M · 0.4 GB59 devices with unified memory can run Supra Title 50M Pre, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Supra Title 50M Pre
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Supra Title 50M Pre need?
Supra Title 50M Pre requires 0.4 GB of VRAM at Q4_K_M, or 0.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 52M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.4 GB (at full 5K context)
VRAM usage by quantization
Q4_K_M0.4 GBQ4_K_M + full context0.4 GB- What's the best quantization for Supra Title 50M Pre?
For Supra Title 50M Pre, Q4_K_M (0.4 GB) offers the best balance of quality and VRAM usage. Q5_1 (0.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 0.3 GB.
VRAM requirement by quantization
IQ2_XS0.3 GBIQ4_XS0.3 GBIQ3_XS0.3 GBQ4_K_M ★0.4 GBQ5_K_S0.4 GBBF160.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Supra Title 50M Pre on a Mac?
Supra Title 50M Pre requires at least 0.3 GB at IQ2_XS, 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 Supra Title 50M Pre locally?
Yes — Supra Title 50M Pre can run locally on consumer hardware. At Q4_K_M quantization it needs 0.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Supra Title 50M Pre?
At Q4_K_M, Supra Title 50M Pre can reach ~12222 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1820 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 ÷ 0.4 × 0.65 = ~14444 tok/s
Estimated speed at Q4_K_M (0.4 GB)
~14444 tok/s~1820 tok/s~14444 tok/s~12222 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Supra Title 50M Pre?
At Q4_K_M, the download is about 0.03 GB. The full-precision BF16 version is 0.10 GB. The smallest option (IQ2_XS) is 0.02 GB.
- Which GPUs can run Supra Title 50M Pre?
50 consumer GPUs can run Supra Title 50M Pre at Q4_K_M (0.4 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 Supra Title 50M Pre?
59 devices with unified memory can run Supra Title 50M Pre at Q4_K_M (0.4 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.