Trek Nepal — Hardware Requirements & GPU Compatibility
Chat
Trek Nepal is a 3.2B-parameter open language model from Blazewild. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 2.46 GB of VRAM — see which GPUs and Macs can run it below.
1 likesSpecifications
- Publisher
- Blazewild
- Parameters
- 3.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Trek Nepal Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.9 GB | 16.7 GB | 1.37 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.1 GB | 16.9 GB | 1.57 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 2.5 GB | 17.3 GB | 1.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.8 GB | 17.6 GB | 2.29 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 3.2 GB | 18.0 GB | 2.65 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 3.8 GB | 18.6 GB | 3.21 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 7.0 GB | 21.8 GB | 6.43 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 Trek Nepal?
Q4_K_M · 2.5 GBTrek Nepal (Q4_K_M) requires 2.5 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 131K context window can add up to 14.8 GB, bringing total usage to 17.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Trek Nepal?
Q4_K_M · 2.5 GB59 devices with unified memory can run Trek Nepal, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Trek Nepal need?
Trek Nepal requires 2.5 GB of VRAM at Q4_K_M, or 7.0 GB at BF16. Full 131K context adds up to 14.8 GB (17.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.2B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.4 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M2.5 GBQ4_K_M + full context17.3 GB- What's the best quantization for Trek Nepal?
For Trek Nepal, Q4_K_M (2.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.9 GB.
VRAM requirement by quantization
Q2_K1.9 GBQ4_K_M ★2.5 GBQ5_K_M2.8 GBQ6_K3.2 GBQ8_03.8 GBBF167.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Trek Nepal on a Mac?
Trek Nepal requires at least 1.9 GB at Q2_K, 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 Trek Nepal locally?
Yes — Trek Nepal can run locally on consumer hardware. At Q4_K_M quantization it needs 2.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Trek Nepal?
At Q4_K_M, Trek Nepal can reach ~1789 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~266 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 ÷ 2.5 × 0.65 = ~2114 tok/s
Estimated speed at Q4_K_M (2.5 GB)
~2114 tok/s~266 tok/s~2114 tok/s~1789 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Trek Nepal?
At Q4_K_M, the download is about 1.93 GB. The full-precision BF16 version is 6.43 GB. The smallest option (Q2_K) is 1.37 GB.
- Which GPUs can run Trek Nepal?
50 consumer GPUs can run Trek Nepal at Q4_K_M (2.5 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 Trek Nepal?
59 devices with unified memory can run Trek Nepal at Q4_K_M (2.5 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.