China Telecom, China · Released September 16, 2026Xing4.0 29B-A4B at a glance
- Made by
- China Telecom, China
- Released
- September 16, 2026
- Parameters
- 31.2B, 4B active
- Context window
- 256K tokens
Xing4.0 29B-A4B VRAM Requirements
China Telecom, China · Released September 16, 2026
Xing4.0 29B-A4B has 31.2B parameters (4B active per token). In BF16 its weights alone take 55.0 GiB; quantized to 4-bit, about 16.7 GiB. Each 32K-token request adds 1.41 GiB of KV cache. Cheapest way to run it today: 1 × GeForce RTX 3090 (24 GB) at $0.134 per hour.
Size your own setup in the calculator →VRAM by precision and context length
Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.
| Weights format | Weights only | + 8K context | + 32K context | + 128K context | + 256K context |
|---|---|---|---|---|---|
| BF16 / FP16 | 55.0 | 55.3 | 56.4 | 60.6 | 66.2 |
| FP8 / INT8 | 28.4 | 28.7 | 29.8 | 34.0 | 39.6 |
| 4-bit (GGUF Q4_K_M) | 16.7 | 17.0 | 18.1 | 22.3 | 27.9 |
How many GPUs to run Xing4.0 29B-A4B
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
| GPU | BF16 / FP16 | 8-bit | 4-bit |
|---|---|---|---|
| GeForce RTX 4090 24 GB | 3 | 2 | 1 |
| GeForce RTX 5090 32 GB | 2 | 1 | 1 |
| RTX PRO 6000 Blackwell 96 GB | 1 | 1 | 1 |
| Mac M5 Max 128 GB 96 GB usable | 1 | 1 | 1 |
| Ryzen AI Max+ 395 128 GB 96 GB usable | 1 | 1 | 1 |
| DGX Spark (GB10) 128 GB | 1 | 1 | 1 |
| NVIDIA H100 80 GB | 1 | 1 | 1 |
| NVIDIA H200 141 GB | 1 | 1 | 1 |
| NVIDIA B200 (HGX) 180 GB | 1 | 1 | 1 |
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.
Cheapest way to run Xing4.0 29B-A4B today
For each GPU: the fewest cards that fit Xing4.0 29B-A4B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-19).
| Setup | Per hour | Weights | Provider | Per month | Rent |
|---|---|---|---|---|---|
| 1 × GeForce RTX 3090 24 GB | $0.134 | Q4_K_M | Vast.ai | $97.82 | Rent 1 × GeForce RTX 3090 on Vast.ai (opens in a new tab) → |
| 2 × RTX A4000 16 GB | $0.176 | Q4_K_M | Vast.ai | $128.48 | Rent 2 × RTX A4000 on Vast.ai (opens in a new tab) → |
| 1 × RTX A5000 24 GB | $0.214 | Q4_K_M | Vast.ai | $156.22 | Rent 1 × RTX A5000 on Vast.ai (opens in a new tab) → |
| 1 × RTX PRO 4000 Blackwell 24 GB | $0.243 | Q4_K_M | Vast.ai | $177.39 | Rent 1 × RTX PRO 4000 Blackwell on Vast.ai (opens in a new tab) → |
| 1 × RTX A4500 20 GB | $0.250 | Q4_K_M | RunPod | $182.50 | Rent 1 × RTX A4500 on RunPod (opens in a new tab) → |
| 1 × RTX 4000 Ada 20 GB | $0.280 | Q4_K_M | RunPod | $204.40 | Rent 1 × RTX 4000 Ada on RunPod (opens in a new tab) → |
| 2 × GeForce RTX 4080 / 4080 SUPER 16 GB | $0.300 | Q4_K_M | Akash Network | $219.00 | Rent 2 × GeForce RTX 4080 / 4080 SUPER on Akash Network (opens in a new tab) → |
| 1 × RTX A6000 48 GB | $0.334 | Q8_0 | Vast.ai | $243.82 | Rent 1 × RTX A6000 on Vast.ai (opens in a new tab) → |
On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.
KV cache: how memory grows with context
Architecture: MoE 64 experts + 1 shared · MLA (512 + 64 latent) on 40 layers · mHC hyper-connections. Its cache stays compact because MLA stores one compressed latent per token and layer instead of full keys and values. Only 4B parameters are active per token, but all 31B must sit in memory: VRAM depends on the total, speed on the active part.
| Context per request | BF16 cache | FP8 cache |
|---|---|---|
| 8K tokens | 0.35 GiB | 0.18 GiB |
| 32K tokens | 1.41 GiB | 0.70 GiB |
| 128K tokens | 5.63 GiB | 2.81 GiB |
| 256K tokens | 11.3 GiB | 5.63 GiB |
Xing4.0 29B-A4B VRAM FAQ
How much VRAM does Xing4.0 29B-A4B need?
In BF16 the weights alone take 55.0 GiB (59 GB). In FP8 that is 28.4 GiB, and about 16.7 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 0.35 GiB at 8K tokens and 1.41 GiB at 32K (BF16 cache).
Can Xing4.0 29B-A4B run on a single RTX 4090 (24 GB)?
Yes. In Q4_K_M it fits on one RTX 4090 with an 8K-token context (17.0 GiB including the cache, with llama.cpp).
Can Xing4.0 29B-A4B run on an RTX 5090 (32 GB)?
Yes. In Q8_0 (28.7 GiB with an 8K-token context) it fits on one RTX 5090, with llama.cpp.
Can Xing4.0 29B-A4B run on a Mac?
Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 32 GB of unified memory or more, of which macOS gives the GPU about 21 GB, for one request with an 8K-token context. In 8-bit (Q8_0) it needs 48 GB or more.
Can Xing4.0 29B-A4B run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?
Yes. In BF16 (55.3 GiB with an 8K-token context) it fits on a DGX Spark (128 GB) and on a Ryzen AI Max+ 395 with 128 GB, whose GPU gets up to 96 GB, with llama.cpp and one request.
What is the cheapest way to run Xing4.0 29B-A4B?
On 2026-10-19, the cheapest on-demand setup is 1 × GeForce RTX 3090 (24 GB) with Q4_K_M weights on Vast.ai, at $0.134 per hour (about $97.82 per month). Next: 2 × RTX A4000 (16 GB) with Q4_K_M weights on Vast.ai, at $0.176 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Xing4.0 29B-A4B need?
With one request and an 8K-token context: 1 in BF16, 1 in FP8 and 1 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Xing4.0 29B-A4B VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/xing4-0-29b-a4b.json.
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Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.