Llama 160M — Hardware Requirements & GPU Compatibility
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Llama 160M is a 162M-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 0.47 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 · ~1394 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~153 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 160M
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/afrideva/llama-160m-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.5 GB free · 2K context
Download: llama-160m.q4_k_m.gguf0.10 GBafrideva
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~1262 tok/s
- generation speed
- Cost per 1M output tokens
- $0.027
- per 1M output tokens
How Much VRAM Does Llama 160M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~1489 tok/s | |
| 0.5 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~1456 tok/s | |
| 0.5 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~1394 tok/s | |
| 0.5 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~1337 tok/s | |
| 0.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~1285 tok/s | |
| 0.5 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~1213 tok/s | |
| 0.7 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~936 tok/s |
Selected Q4_K_M
4-bit medium quantization0.5GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~1394 tok/s
≥50 tok/s needs ≥36 GB/s · RTX 5090 ~2478, RTX 3090 Ti ~1394
Which Hardware Can Run Llama 160M?
Q4_K_M · 0.5 GBLlama 160M (Q4_K_M) 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. 163 devices with unified memory can run Llama 160M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1052Simulate output on RTX 3080RTX 2080 Ti~852Simulate output on RTX 2080 TiRTX 3070 Ti~841Simulate output on RTX 3070 TiM3*8 GB~153Simulate output on iMac 24" M3 (8 GB)Orin Nano~141Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~102Simulate output on iPhone 1720 GB
fits · 19.5 GB free · up to 2K ctx
1 GPU
RX 7900 XT~1021Simulate output on RX 7900 XT32–40 GB
fits · 31.5 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2478Simulate output on RTX 5090M5 Max36 GB~685Simulate output on Mac Studio M5 Max (36 GB)M532 GB~229Simulate output on MacBook Air 13" M5 (32 GB)Orin~283Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~187Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Llama 160M
Llama family · 20 models →Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Nandi Mini 150MFrontiersMind | — | 0.6 GB | ~1040 | same size |
| Llm Jp 3 150Mllm-jp | — | 0.5 GB | ~1489 | same size |
| Rwkv 4 169M PileRWKV | — | 1 GB | ~5956 | same size |
Specifications
- Publisher
- JackFram
- Family
- Llama
- Parameters
- 162M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-05-26
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Llama 160M need?
Llama 160M requires 0.5 GB of VRAM at Q4_K_M, or 0.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 162M × 4.8 bits ÷ 8 = 0.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
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 160M?
For Llama 160M, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 160M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Llama 160M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 160M locally?
Yes — Llama 160M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 160M?
At Q4_K_M, Llama 160M 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 ÷ 0.47 × 0.65 = ~11064 tok/s
Estimated speed at Q4_K_M (0.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 160M?
At Q4_K_M, the download is about 0.10 GB. The full-precision BF16 version is 0.32 GB. The smallest option (Q2_K) is 0.07 GB.
Which GPUs can run Llama 160M?
52 consumer GPUs can run Llama 160M 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 160M?
163 devices with unified memory can run Llama 160M at Q4_K_M (0.5 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.