MSI EDGEXPERT MS-C931, GB10, 128GB, 4TB

BUY NOW AT NVIDIA →

product: MSI EdgeXpert MS-C931, GB10, 128GB, 4TB
category: Workstations
brand: MSI
memory: 128GB unified · 273 GB/s [2]
notes: GB10 Grace Blackwell. Price from NVIDIA Marketplace (first-party NVIDIA store).
price:
  now: $3,999.99 (NVIDIA, official list, 2026-01-24) [1] BUY NOW AT NVIDIA →
  since_first: FLAT SINCE DEC 14, 2025
  first_record: 2025-12-14 (price in the paid export)
  change:
    1d: —
    7d: —
    14d: —
    30d: —
    90d: —
    180d: —
  availability: in stock

FLAT SINCE DEC 14, 2025 — now $3,999.99. BUY NOW AT NVIDIA →

PRICE TREND (shape since first record)

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               2025-12-14                                        2026-11-13
               █ recorded price  ░ gap between records · shape only; past prices are in the paid export

PRICE HISTORY (last 7 days)

no price recorded in the last 7 days; the current price above is the latest record

3 older records back to Dec 14, 2025 (every price, date and source) are in the paid export (YAML / CSV).

PAIRS WELL WITH (add-ons, not price-tracked · all peripherals)
APC Back-UPS Pro 1500VA (BR1500MS2):
  why: Keeps a long run or a model download alive through a short outage.
  buy: BUY ON AMAZON →
Logitech MX Keys S keyboard:
  why: Multi-device keyboard for a headless AI box plus a laptop.
  buy: BUY ON AMAZON →
Logitech MX Master 3S mouse:
  why: Multi-device mouse that switches between three computers.
  buy: BUY ON AMAZON →
OWC Thunderbolt 10G Ethernet adapter:
  why: 10GbE to a NAS so model files load fast, on machines without built-in 10GbE.
  buy: BUY ON AMAZON →

CAN RUN — LOCAL MODELS THAT FIT

memory: 128GB unified · usable for models ≈ 96 GB (rule)
bandwidth: 273 GB/s [2]
largest_fit: Nemotron 3.5 Super 120B Q4_0
fits: 29 of 33 model files (excluding the Llama 2 7B reference)
note: fit = weights + KV cache at 8,192 tokens + 10% runtime overhead
Nemotron 3.5 Super 120B-A12B (MoE)Q8_0128.5 GB141.4 GB [3]nono published run
Nemotron 3.5 Super 120B-A12B (MoE)Q4_072.1 GB79.4 GB [3]YESno published run
gpt-oss 120B (MoE)MXFP463.4 GB70.0 GB [5]YES58.7 tok/s tg32 [4]
Llama 4 Scout 109B-A17B (MoE)Q8_0114.5 GB127.6 GB [6]TIGHTno published run
Llama 4 Scout 109B-A17B (MoE)Q4_K_M65.4 GB73.5 GB [6]YESno published run
Nemotron 3.5 Bolt 74B-A9B (MoE)BF16148.2 GB163.1 GB [7]nono published run
Nemotron 3.5 Bolt 74B-A9B (MoE)Q8_078.9 GB86.8 GB [7]YESno published run
Nemotron 3.5 Bolt 74B-A9B (MoE)Q4_K_XL50.2 GB55.3 GB [7]YESno published run
Llama 3.3 70Bf16141.1 GB157.9 GB [8]nono published run
Llama 3.3 70BQ8_075.0 GB85.2 GB [8]YESno published run
Llama 3.3 70BQ4_K_M42.5 GB49.5 GB [8]YESno published run
OLMo 3.1 32Bbf1664.5 GB72.3 GB [9]YESno published run
OLMo 3.1 32BQ8_034.3 GB39.0 GB [9]YESno published run
OLMo 3.1 32BQ4_K_M19.5 GB22.8 GB [9]YESno published run
Nemotron 3.5 Lightning 30B-A3B (MoE)BF1663.2 GB69.5 GB [10]YESno published run
Nemotron 3.5 Lightning 30B-A3B (MoE)Q8_033.6 GB37.0 GB [10]YESno published run
Nemotron 3.5 Lightning 30B-A3B (MoE)Q4_018.9 GB20.8 GB [10]YESno published run
Gemma 4 31BBF1661.4 GB69.1 GB [11]YESno published run
Gemma 4 31BQ8_032.6 GB37.4 GB [11]YESno published run
Gemma 4 31BQ4_K_M18.3 GB21.7 GB [11]YESno published run
Granite 4.2 30Bbf1658.6 GB66.6 GB [12]YESno published run
Granite 4.2 30BQ8_031.1 GB36.4 GB [12]YESno published run
Granite 4.2 30BQ4_K_M17.7 GB21.6 GB [12]YESno published run
Gemma 4 26B-A4B (MoE)BF1650.5 GB55.9 GB [13]YESno published run
Gemma 4 26B-A4B (MoE)Q8_026.9 GB29.9 GB [13]YESno published run
Gemma 4 26B-A4B (MoE)Q4_K_M16.9 GB19.0 GB [13]YESno published run
gpt-oss 20BMXFP412.1 GB13.5 GB [14]YES83.4 tok/s tg32 [4]
Phi-4 14BF1629.3 GB33.9 GB [15]YESno published run
Phi-4 14BQ8_015.6 GB18.8 GB [15]YESno published run
Phi-4 14BQ4_K_M8.9 GB11.5 GB [15]YESno published run
Gemma 4 12BBF1623.8 GB26.7 GB [16]YESno published run
Gemma 4 12BQ8_012.7 GB14.4 GB [16]YESno published run
Gemma 4 12BQ4_K_M7.1 GB8.3 GB [16]YESno published run
Llama 2 7B (llama.cpp reference benchmark model)Q8_07.2 GB12.2 GB [17]YESno published run
Llama 2 7B (llama.cpp reference benchmark model)Q4_03.8 GB8.5 GB [17]YES58.5 tok/s tg128 [18]

WHERE TO BUY

official: BUY NOW → NVIDIA
retailers: not tracked yet (partner programs pending)
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SOURCES

Current sources only. Every older price point and its source is in the paid export.

#whatsourcefetched
[1]MSI EdgeXpert $3,999.99 on 2026-01-24 — Wayback snapshot 20260124 of https://marketplace.nvidia.com/en-us/e...web.archive.org/web/20260124053959/marketplace.nvidia.com/en-us/ent...2026-11-13
[2]NVIDIA GB10 platform: 'Memory Bandwidth 273 GB/s'www.nvidia.com/en-us/products/workstations/dgx-spark/2026-11-13
[3]Nemotron 3.5 Super 120B-A12B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF2026-11-13
[4]Measured on a DGX Spark (same GB10 chip and 273 GB/s), not this exact boxgithub.com/ggml-org/llama.cpp/blob/master/benches/dgx-spark/dgx-spa...2026-11-13
[5]gpt-oss 120B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/ggml-org/gpt-oss-120b-GGUF2026-11-13
[6]Llama 4 Scout 109B-A17B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF2026-11-13
[7]Nemotron 3.5 Bolt 74B-A9B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF2026-11-13
[8]Llama 3.3 70B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF2026-11-13
[9]OLMo 3.1 32B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF2026-11-13
[10]Nemotron 3.5 Lightning 30B-A3B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF2026-11-13
[11]Gemma 4 31B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/gemma-4-31B-it-GGUF2026-11-13
[12]Granite 4.2 30B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/ibm-granite/granite-4.2-30b-GGUF2026-11-13
[13]Gemma 4 26B-A4B (MoE): GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF2026-11-13
[14]gpt-oss 20B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/ggml-org/gpt-oss-20b-GGUF2026-11-13
[15]Phi-4 14B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/phi-4-GGUF2026-11-13
[16]Gemma 4 12B: GGUF sizes + config.json (KV cache at 8k)huggingface.co/unsloth/gemma-4-12b-it-GGUF2026-11-13
[17]Llama 2 7B (llama.cpp reference benchmark model): GGUF sizes + config.json (KV cache at 8k)huggingface.co/TheBloke/Llama-2-7B-GGUF2026-11-13
[18]Vulkan scoreboard row 'Nvidia GB10' (chip-level result, not this exact box)github.com/ggml-org/llama.cpp/discussions/108792026-11-13