JoyAI LLM Flash — Hardware Requirements & GPU Compatibility
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JoyAI LLM Flash is a 49.3B-parameter open language model from jdopensource. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 30.54 GB of VRAM — see which GPUs and Macs can run it below.
1.8K downloads
159 likesSpecifications
- Publisher
- jdopensource
- Parameters
- 49.3B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2026-02-14
Get Started
HuggingFace
How Much VRAM Does JoyAI LLM Flash Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 21.9 GB | 64.2 GB | 20.95 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 25 GB | 67.3 GB | 24.03 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 30.5 GB | 72.8 GB | 29.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 36.1 GB | 78.4 GB | 35.12 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 41.6 GB | 83.9 GB | 40.66 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 50.3 GB | 92.5 GB | 49.29 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 99.5 GB | 141.8 GB | 98.57 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 JoyAI LLM Flash?
Q4_K_M · 30.5 GBJoyAI LLM Flash (Q4_K_M) requires 30.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 40+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 72.8 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run JoyAI LLM Flash?
Q4_K_M · 30.5 GB31 devices with unified memory can run JoyAI LLM Flash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does JoyAI LLM Flash need?
JoyAI LLM Flash requires 30.5 GB of VRAM at Q4_K_M, or 99.5 GB at BF16. Full 131K context adds up to 42.3 GB (72.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 49.3B × 4.8 bits ÷ 8 = 29.6 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.2 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M30.5 GBQ4_K_M + full context72.8 GB- Can NVIDIA GeForce RTX 4090 run JoyAI LLM Flash?
Yes, at Q2_K (21.9 GB) or lower. Higher quantizations like Q3_K_M (25 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for JoyAI LLM Flash?
For JoyAI LLM Flash, Q4_K_M (30.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (36.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 21.9 GB.
VRAM requirement by quantization
Q2_K21.9 GBQ4_K_M ★30.5 GBQ5_K_M36.1 GBQ6_K41.6 GBQ8_050.3 GBBF1699.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run JoyAI LLM Flash on a Mac?
JoyAI LLM Flash requires at least 21.9 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 JoyAI LLM Flash locally?
Yes — JoyAI LLM Flash can run locally on consumer hardware. At Q4_K_M quantization it needs 30.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is JoyAI LLM Flash?
At Q4_K_M, JoyAI LLM Flash can reach ~144 tok/s on AMD Instinct MI350X. 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 ÷ 30.5 × 0.65 = ~170 tok/s
Estimated speed at Q4_K_M (30.5 GB)
~170 tok/s~170 tok/s~144 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of JoyAI LLM Flash?
At Q4_K_M, the download is about 29.57 GB. The full-precision BF16 version is 98.57 GB. The smallest option (Q2_K) is 20.95 GB.
- Which GPUs can run JoyAI LLM Flash?
1 consumer GPU can run JoyAI LLM Flash at Q4_K_M (30.5 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run JoyAI LLM Flash?
35 devices with unified memory can run JoyAI LLM Flash at Q4_K_M (30.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.