GPT J 6B — Hardware Requirements & GPU Compatibility
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GPT J 6B is a 6B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 3.96 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 · ~166 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~18 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT J 6B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve EleutherAI/gpt-j-6b --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~59 tok/s
- generation speed
- Cost per 1M output tokens
- $0.39
- per 1M output tokens
How Much VRAM Does GPT J 6B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.8 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~233 tok/s | |
| 3.2 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~204 tok/s | |
| 4.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~166 tok/s | |
| 4.7 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~139 tok/s | |
| 5.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~120 tok/s | |
| 6.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~99 tok/s | |
| 13.2 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~50 tok/s |
Selected Q4_K_M
4-bit medium quantization4.0GBat 2K context
- Weights
- 3.3 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.0 GB free · ~166 tok/s
≥50 tok/s needs ≥304 GB/s · RTX 5090 ~294, RTX 3090 Ti ~166
Which Hardware Can Run GPT J 6B?
Q4_K_M · 4.0 GBGPT J 6B (Q4_K_M) requires 4.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ 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 GPT J 6B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.0 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~125Simulate output on RTX 3080RTX 2080 Ti~101Simulate output on RTX 2080 TiRTX 3070 Ti~100Simulate output on RTX 3070 TiM3*8 GB~18Simulate output on iMac 24" M3 (8 GB)Orin Nano~17Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~12Simulate output on iPhone 1720 GB
fits · 16.0 GB free · up to 2K ctx
1 GPU
RX 7900 XT~121Simulate output on RX 7900 XT32–40 GB
fits · 28.0 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~294Simulate output on RTX 5090M5 Max36 GB~81Simulate output on Mac Studio M5 Max (36 GB)M532 GB~27Simulate output on MacBook Air 13" M5 (32 GB)Orin~34Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~22Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is GPT J 6B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 7 with a result for this model; badges show who ran the test.
- MMLU
Epoch AI · Aug 2021Lab-reported
Lab-reported25.7%
#84/85 open
- HellaSwag
Epoch AI · Aug 2021Lab-reported
Lab-reported66.2%
#38/47 open
- ARC Challenge
Epoch AI · Aug 2021Lab-reported
Lab-reported36.3%
#47/56 open
- WinoGrande
Epoch AI · Aug 2021Lab-reported
Lab-reported64.5%
#37/51 open
- BoolQ
Epoch AI · Aug 2021Lab-reported
Lab-reported65.4%
#30/38 open
- OpenBookQA
Epoch AI · Aug 2021Lab-reported
Lab-reported38.2%
#21/26 open
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Yi 6B Chat01.AI | — | 4.2 GB | ~161 | same size |
| Chatglm3 6BZ.ai | — | 5.2 GB | ~159 | same size |
| T5gemma 2B 2B Ul2Google | — | 6 GB | ~178 | same size |
| Yi 1.5 6B Chat01.AI | — | 4.2 GB | ~161 | same size |
Specifications
- Publisher
- EleutherAI
- Parameters
- 6B
- Architecture
- GPTJForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,400
- Release Date
- 2022-03-02
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does GPT J 6B need?
GPT J 6B requires 4.0 GB of VRAM at Q4_K_M, or 13.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6B × 4.8 bits ÷ 8 = 3.6 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 GPT J 6B?
For GPT J 6B, Q4_K_M (4.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT J 6B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run GPT J 6B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run GPT J 6B locally?
Yes — GPT J 6B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT J 6B?
At Q4_K_M, GPT J 6B can reach ~1212 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~166 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 ÷ 3.96 × 0.65 = ~1313 tok/s
Estimated speed at Q4_K_M (4.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GPT J 6B?
At Q4_K_M, the download is about 3.60 GB. The full-precision BF16 version is 12.00 GB. The smallest option (Q2_K) is 2.55 GB.
Which GPUs can run GPT J 6B?
52 consumer GPUs can run GPT J 6B 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 GPT J 6B?
163 devices with unified memory can run GPT J 6B at Q4_K_M (4.0 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.