Llama 68M — Hardware Requirements & GPU Compatibility
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Llama 68M is a 68M-parameter open language model from JackFram in the Llama family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.04 GB of VRAM — see which GPUs and Macs can run it below.
186.6K downloads
39 likesSpecifications
- Publisher
- JackFram
- Family
- Llama
- Parameters
- 68M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-07-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Llama 68M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.0 GB | — | 0.03 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.0 GB | — | 0.03 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.0 GB | — | 0.03 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.0 GB | — | 0.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.1 GB | — | 0.05 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.1 GB | — | 0.06 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.1 GB | — | 0.07 GB | 8-bit quantization, near-lossless |
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 Llama 68M?
Q4_K_M · 0.0 GBLlama 68M (Q4_K_M) requires 0.0 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 Llama 68M?
Q4_K_M · 0.0 GB59 devices with unified memory can run Llama 68M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Llama 68M
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Llama 68M need?
Llama 68M requires 0.0 GB of VRAM at Q4_K_M, or 0.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 68M × 4.8 bits ÷ 8 = 0 GB
VRAM usage by quantization
Q4_K_M0.0 GB- What's the best quantization for Llama 68M?
For Llama 68M, Q4_K_M (0.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.0 GB.
VRAM requirement by quantization
Q2_K0.0 GBQ3_K_L0.0 GBQ4_K_M ★0.0 GBQ5_K_S0.1 GBQ5_K_M0.1 GBBF160.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 68M on a Mac?
Llama 68M requires at least 0.0 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 Llama 68M locally?
Yes — Llama 68M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 68M?
At Q4_K_M, Llama 68M can reach ~110000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~16380 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 ÷ 0.0 × 0.65 = ~130000 tok/s
Estimated speed at Q4_K_M (0.0 GB)
~130000 tok/s~16380 tok/s~130000 tok/s~110000 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 68M?
At Q4_K_M, the download is about 0.04 GB. The full-precision BF16 version is 0.14 GB. The smallest option (Q2_K) is 0.03 GB.
- Which GPUs can run Llama 68M?
50 consumer GPUs can run Llama 68M at Q4_K_M (0.0 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 Llama 68M?
59 devices with unified memory can run Llama 68M at Q4_K_M (0.0 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.