Llama 68M — Hardware Requirements & GPU Compatibility
Chat
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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~16380 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~1792 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 68M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/llama-68m-GGUF:Q4_K_MQ4_K_M · 2K context
Download: llama-68m.Q4_K_M.gguf0.05 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (12 GB headroom) · Q4_K_M
- Generation speed
- ~5850 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0039
- per 1M output tokens
How Much VRAM Does Llama 68M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~21840 tok/s | |
| 0.0 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~21840 tok/s | |
| 0.0 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~16380 tok/s | |
| 0.0 GB | — | 3-bit large — 3-bit large quantization | Fits: 2K context · ~16380 tok/s | |
| 0.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~16380 tok/s | |
| 0.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~13104 tok/s | |
| 0.1 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~10920 tok/s | |
| 0.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~9360 tok/s |
Selected Q4_K_M
4-bit medium quantization0.0GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 24.0 GB free · ~16380 tok/s
≥50 tok/s needs ≥3 GB/s · RTX 5090 ~29120, RTX 3090 Ti ~16380
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Llama 68M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 8.0 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~12355Simulate output on RTX 3080RTX 2080 Ti~10010Simulate output on RTX 2080 TiRTX 3070 Ti~9885Simulate output on RTX 3070 TiM3*8 GB~1792Simulate output on iMac 24" M3 (8 GB)Orin Nano~1658Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~1194Simulate output on iPhone 1720 GB
fits · 20.0 GB free · up to 2K ctx
1 GPU
RX 7900 XT~12000Simulate output on RX 7900 XT32–40 GB
fits · 32.0 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~29120Simulate output on RTX 5090M5 Max36 GB~8050Simulate output on Mac Studio M5 Max (36 GB)M532 GB~2688Simulate output on MacBook Air 13" M5 (32 GB)Orin~3328Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~2194Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 68M
Llama family · 19 models →Specifications
- 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
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
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 68M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Llama 68M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 68M locally?
Yes — Llama 68M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 68M?
At Q4_K_M, Llama 68M can reach ~120000 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 ÷ 0.04 × 0.65 = ~130000 tok/s
Estimated speed at Q4_K_M (0.0 GB)
Real-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?
52 consumer GPUs can run Llama 68M at Q4_K_M. 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 Llama 68M?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.