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.
273.4K downloads
1.5K likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 6B
- Architecture
- GPTJForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,400
- Release Date
- 2023-06-21
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT J 6B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.8 GB | — | 2.55 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.9 GB | — | 2.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.2 GB | — | 2.92 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.3 GB | — | 3.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.0 GB | — | 3.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 4.7 GB | — | 4.28 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 5.5 GB | — | 4.95 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 6.6 GB | — | 6.00 GB | 8-bit quantization, near-lossless |
Which GPUs 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run GPT J 6B?
Q4_K_M · 4.0 GB33 devices with unified memory can run GPT J 6B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 2 →Related Models
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 6.6 GB at Q8_0.
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)
VRAM usage by quantization
Q4_K_M4.0 GB- 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_0 (4.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.8 GB.
VRAM requirement by quantization
Q2_K2.8 GBQ4_03.3 GBQ4_K_M ★4.0 GBQ5_04.1 GBQ5_K_S4.5 GBQ8_06.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT J 6B on a Mac?
GPT J 6B requires at least 2.8 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 GPT J 6B locally?
Yes — GPT J 6B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT J 6B?
At Q4_K_M, GPT J 6B can reach ~736 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 4.0 × 0.55 = ~736 tok/s
Estimated speed at Q4_K_M (4.0 GB)
~736 tok/s~166 tok/s~550 tok/s~455 tok/sReal-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 Q8_0 version is 6.00 GB. The smallest option (Q2_K) is 2.55 GB.
- Which GPUs can run GPT J 6B?
35 consumer GPUs can run GPT J 6B at Q4_K_M (4.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT J 6B?
33 devices with unified memory can run GPT J 6B at Q4_K_M (4.0 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.