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.
242.5K downloads
1.5K likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 6B
- Architecture
- GPTJForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,400
- Release Date
- 2022-03-02
- 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_Kest. | 3.40 | 2.8 GB | — | 2.55 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.2 GB | — | 2.92 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 4.0 GB | — | 3.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.7 GB | — | 4.28 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 5.5 GB | — | 4.95 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 6.6 GB | — | 6.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 13.2 GB | — | 12.00 GB | Brain floating point 16 — preferred for training |
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 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. 50 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 GB59 devices with unified memory can run GPT J 6B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
Benchmark details →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 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)
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_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
Q2_K2.8 GBQ4_K_M ★4.0 GBQ5_K_M4.7 GBQ6_K5.5 GBQ8_06.6 GBBF1613.2 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 ~1111 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 ÷ 4.0 × 0.65 = ~1313 tok/s
Estimated speed at Q4_K_M (4.0 GB)
~1313 tok/s~166 tok/s~1313 tok/s~1111 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 BF16 version is 12.00 GB. The smallest option (Q2_K) is 2.55 GB.
- Which GPUs can run GPT J 6B?
50 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. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT J 6B?
59 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, 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.