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.
Runs well on a 24 GB GPU
Runs on 24 GB (Q2_K · ~258 tok/s · 32K)Macs from 32 GB (Q3_K_M · ~80 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run JoyAI LLM Flash
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve jdopensource/JoyAI-LLM-Flash --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on A40 (48 GB) (17 GB headroom) · Q4_K_M
- Generation speed
- ~179 tok/s
- generation speed
- Cost per 1M output tokens
- $0.54
- per 1M output tokens
How Much VRAM Does JoyAI LLM Flash Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 21.9 GB | 22.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~258 tok/s | |
| 25 GB | 25.8 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 25.1 GB | |
| 30.5 GB | 31.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 30.6 GB | |
| 36.1 GB | 36.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 36.2 GB | |
| 41.6 GB | 42.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 41.7 GB | |
| 50.3 GB | 51.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 50.3 GB | |
| 99.5 GB | 100.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 99.6 GB |
Selected Q4_K_M
4-bit medium quantization30.6GBat 16K context
- Weights
- 29.6 GB
- KV cache
- 0.8 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 6.6 GB more, Q2_K fits
Which Hardware 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 5.4 GB, bringing total usage to 35.9 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090. 78 devices with unified memory can run JoyAI LLM Flash, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
≤ 20 GB
≤ 20 GB tier →doesn't fit · smallest quant needs 22.0 GB
32–40 GB
fits · 1.4 GB free · up to 32K ctx
1 GPU · 2 Mac classes · 1 device family
RTX 5090~284Simulate output on RTX 5090M5 Max36 GB~78Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~44Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~73Simulate output on NVIDIA Jetson AGX Orin 32GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mixtral 8x7B Instruct v0.1Mistral AI | 65.1 | 23.5 GB | ~92 | same size |
| Kimi Linear 48B A3B BaseMoonshot AI | — | 23.4 GB | ~275 | same size |
| Llama 3 3 Nemotron Super 49B V1 5NVIDIA | — | 22.8 GB | ~35 | same size |
| Nemotron H 47B Reasoning 128KNVIDIA | — | 107.2 GB | —needs > 24 GB | same size |
| AI21 Jamba Mini 1.5AI21 Labs | — | 117.8 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- jdopensource
- Parameters
- 49.3B
- Active parameters
- 2.5B per token
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2026-02-14
Frequently 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 5.4 GB (35.9 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 6.3 GB (at full 131K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run JoyAI LLM Flash on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run JoyAI LLM Flash, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 30.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (36 GB).
Can I run JoyAI LLM Flash locally?
Yes — JoyAI LLM Flash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is JoyAI LLM Flash?
At Q4_K_M, JoyAI LLM Flash can reach ~120 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.5 GB active ÷ (8000 × 0.65) = 0.295 ms, plus 40 layers × 0.055 ms = 2.200 ms, so 1000 ÷ 2.495 ms = ~401 tok/s
Estimated speed at Q4_K_M (30.5 GB)
Real-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. Top options include NVIDIA GeForce RTX 5090.
Which devices can run JoyAI LLM Flash?
78 devices with unified memory can run JoyAI LLM Flash at Q4_K_M (30.5 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.