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ai-sage / gigachat3_5

GigaChat3.5-432B-A28B-Reasoning

438.1 billion parameters, routing 8 of 256 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
GigaChat3.5-432B-A28B-Reasoning needs about 270.0 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. None of the 18 common devices listed below holds it entirely at this setting. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

The published checkpoint is FP8 block-quantized (quantization_config in config.json); the geometry read here is the architecture's, not the encoding's. Licence declared on the repository: mit.

Weight-file size by format

FormatSizeRangeBasis
FP16876.6 GB613.3 GB – 884.9 GBsize range
Q8_0465.8 GB325.8 GB – 471.9 GBsize range
Q6_K359.8 GB251.6 GB – 365.1 GBsize range
Q5_K_M313.0 GB210.8 GB – 365.1 GBsize range
Q5_0305.7 GB210.8 GB – 365.1 GBsize range
Q4_K_M268.9 GB172.5 GB – 365.1 GBsize range
Q4_0254.9 GB172.5 GB – 365.1 GBsize range
Q3_K_M219.1 GB131.8 GB – 365.1 GBsize range
Q2_K173.5 GB100.6 GB – 365.1 GBsize range

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 0 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 5070270.0 GB of 12 GB39 layers on system RAM~5.1 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT270.0 GB of 16 GB38 layers on system RAM~5.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 4080270.0 GB of 16 GB38 layers on system RAM~5.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 5080270.0 GB of 16 GB38 layers on system RAM~5.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti270.0 GB of 16 GB38 layers on system RAM~5.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB270.0 GB of 16 GB38 layers on system RAM~5.2 tok/s with offloadOpen →
NVIDIA GeForce RTX 4090270.0 GB of 24 GB37 layers on system RAM~5.4 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX270.0 GB of 24 GB37 layers on system RAM~5.4 tok/s with offloadOpen →
NVIDIA GeForce RTX 3090270.0 GB of 24 GB37 layers on system RAM~5.4 tok/s with offloadOpen →
NVIDIA GeForce RTX 5090270.0 GB of 32 GB36 layers on system RAM~5.5 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation270.0 GB of 48 GB34 layers on system RAM~5.8 tok/s with offloadOpen →
Apple M4 Pro270.0 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro270.0 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S270.0 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max270.0 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max270.0 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark270.0 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra270.0 GB of 192 GBdoes not fitnot calibratedOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs GigaChat3.5-432B-A28B-Reasoning

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
7 configurations in stock · 8 out of stock
MachineSpeedPer hourPer M tokensWhereRent
8× L40S384 GBrecommendedcheapestbest value~340 tok/sFaster than you read$3.73$3.05Vast.aiMarketplace · 99.9% reliableRent on Vast.ai
8× RTX PRO 5000384 GB~529 tok/sFaster than you read$6.81$3.57Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
2× H200 NVL282 GB~525 tok/sFaster than you read$7.20$3.81Vast.aiMarketplace · 98.7% reliableRent on Vast.ai
1× B300270 GB~428 tok/sFaster than you read$7.89$5.11RunPodSecure CloudRent on RunPod
4× H100 SXM320 GBfastest~706 tok/sFaster than you read$9.07$3.56Vast.aiMarketplace · 99.9% reliableRunPod $13.96/h Rent on Vast.ai
2× H200282 GB~525 tok/sFaster than you read$9.18$4.86RunPodSecure CloudVast.ai $10.00/h Rent on RunPod
4× H100 PCIe320 GB~422 tok/sFaster than you read$9.59$6.31Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of GigaChat3.5-432B-A28B-Reasoning is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of GigaChat3.5-432B-A28B-Reasoning three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-20 at pinned commit 02f55379ac0b; parameter count from the safetensors index at the same revision.

Architecture
gigachat3_5
Layers
40
Hidden size
7,168
Attention heads
64
KV heads
64
Feed-forward width
18,432
Vocabulary
128,256
Context ceiling
262,144
RoPE theta
100,000
Experts
256
Experts / token
8
Expert width
2,048
Shared experts
1
Latent KV rank
512

Where the memory goes

tokenembedding× 40 decoder blocksLatent attentioncompressed KV rankfull contextRouted experts8 of 256 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Latent attention projects keys and values into a compressed rank before caching them, which is why its KV cache is a fraction of a comparable dense model.

Open the exact official config.json ↗

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  -d '{"model":"ai-sage-gigachat3-5-432b-a28b-reasoning","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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  data-model="ai-sage-gigachat3-5-432b-a28b-reasoning" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “GigaChat3.5-432B-A28B-Reasoning VRAM and hardware requirements.” Architecture from ai-sage/GigaChat3.5-432B-A28B-Reasoning at revision 02f55379ac0b, retrieved 2026-09-20. https://llmbottleneck.com/models/ai-sage-gigachat3-5-432b-a28b-reasoning

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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