Qwen3-Coder 30B-A3B on CPU-only workstation (Ryzen 9950X, 192 GB DDR5)

30.532B (3.3B active) on 192 GB at 90 GB/s. Hardware details · Model details

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Yes. Qwen3-Coder 30B-A3B fits on CPU-only workstation (Ryzen 9950X, 192 GB DDR5) at the recommended Q8_0 configuration, requiring approximately 33.5 GB at 8K context. Practical context capacity is 256K. Expected decode for the recommended configuration is ~8.8–12.7 t/s. Estimated
CompatibilityComfortable
Calculated fit
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
Q8_0
Calculated gguf
Memory required
33.5 GB
Calculated of 176.6 GB usable — 19%
Max practical context
256K
Calculated model supports 256K

Memory budget at 8K context

Model weights ⓘ
30.8 GB Calculated
KV cache ⓘ
0.8 GB Calculated
Runtime overhead ⓘ
1.9 GB Estimated
Total required
33.5 GB Calculated
Headroom ⓘ
143.1 GB Calculated
Highest-precision quantization that leaves headroom: uses 19% of usable memory at 8K context.
Discrete GPU: 192 GB of VRAM, of which we assume 92% is usable after driver and context overhead.
Mixture of experts: all 30.532B parameters must be resident in memory even though only ~3.3B are active per token. Memory follows total parameters; speed follows active parameters.
Performance

Estimated performance · recommended Q8_0

Decode, prefill and TTFT below are estimates for Q8_0. We do not have a comparable Q8_0 measurement on this machine.

Estimated decode
~8.8–12.7 t/s
Estimated Q8_0; calculated range
Estimated prefill
~41.4–86.1 t/s
Estimated Q8_0; calculated range
Estimated TTFT at 8K
~95.2–198.0 s
Estimated Q8_0; calculated range
Hardware load reference
190 W
Measured machine-level load; not this model run
How the estimate is calculated
  1. Decode: reading 3.3B active parameters at 8.50 bits/weight takes 78.26 ms at 90 GB/s x 50% achieved efficiency.
  2. MoE routing penalty of 15% applied: expert gathers are less bandwidth-efficient than a dense sweep.
  3. Prefill: 4 TFLOPS (FP16) x 32% assumed model-FLOPs utilisation, divided by 2 x 10.0B parameters per token.
  4. Prefill uses 10.0B effective parameters, not the 3.3B active in decode: a batch of hundreds of tokens routes across most of the expert pool.

  • MoE decode depends on how well the engine batches expert gathers; real results vary more than for dense models.
  • Prefill throughput is highly engine-dependent. Flash attention, batch size and quantized KV all move this number substantially, and for sparse mixture-of-experts models it is the least reliable figure we produce.
  • This is a calculated estimate, not a measurement. It assumes a single request, a short prompt, no speculative decoding and a warm model already resident in memory.

Estimator version estimator_v1. Stored with every estimated row so old estimates can be regenerated when the model improves.

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This model at other quantizations on this machine
QuantizationResidentDiskTotal neededUtilisationFitMax context
Q4_K_M17.8 GB17.8 GB20.0 GB11%Comfortable256K
Q5_K_M20.8 GB20.8 GB23.2 GB13%Comfortable256K
Q8_0recommended30.8 GB30.8 GB33.5 GB19%Comfortable256K
Cost of running Qwen3-Coder 30B-A3B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$21
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $79 less
Net cash at purchase
$1,531
Calculated VAT not reclaimable
Total over 5 years
$1,233
Calculated after tax, after resale
Cost per USD/1M tokens
$15.06
Calculated 1.4M tokens/month

The $100 comparison uses the Codex Pro 5x / Claude Max 5x plans. A fixed planning conversion is used for USD. This compares monthly spend only: subscriptions have usage limits, local hardware has different capabilities and constraints, and taxes or regional pricing may change the charged amount. Prices checked 7 September 2026.

Purchase-price input: $1,531 in United States (federal), tax/VAT excluded · estimated · NL retail (system build) (converted catalogue estimate). This is the localized purchase input; the after-tax economic cost is calculated separately below.

Monthly breakdown

Depreciation
1,301 over 5 years, straight-line to a 230 residual
$21.68
Electricity
11.6 kWh/month at 0.140/kWh
$1.63
Cost of capital
4.0%/yr on 880 average capital employed
$2.93
Monthly cost before tax$26.24
Electricity tax shield
Running costs are deductible business expenses
−$0.34
First-year expensing
§179 (100.0%)
−$5.36
Monthly economic cost after tax$20.54

Three different numbers, deliberately

Cash cost
$1,531
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$21.68/month
$1,301 written down over 5 years to a $230 residual.
After-tax economic cost
$20.54/month
Depreciation plus running costs plus cost of capital, less the tax those deductions save. This is the figure to compare between machines.
Assumptions and sources (verified 2026-09-07)
VAT rate
0% Official spec
VAT recoverable
0% Official spec
Federal C-corporation estimate at the flat 21% rate. Pass-through entities should use the sole-proprietor estimate as a rougher proxy.
Effective deduction rate
21.00% Calculated
Headline marginal rate 21.00%.
Depreciation
5 years, straight-line Assumption
Residual value
$230 (15%) Assumption
Two GPU generations later, the card is worth a fraction of its list price.
Electricity
$0.140/kWh Assumption
Average power draw
66 W Calculated
Load 190 W for 20% of powered hours, idle 35 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $321 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$2.93/month Assumption
Assumes the machine generates tokens 20% of its 176 powered hours per month. Cost per token scales inversely with this number — halve the utilisation and the cost per token doubles.
Federal planning estimate only, not tax advice. State and local income tax, sales/use tax and incentives are excluded. It assumes 100% business use, a Section 179 election, enough business income to use it, and that the full annual limit remains available.
Efficiency metrics
Decode per $1,000 spent
7.0 t/s
Calculated purchase price only
Decode per $100/month
52.4 t/s
Calculated after-tax ownership cost
Tokens per joule
0.16
Calculated same as tokens/s per watt
USD per 1M output tokens
$15.06
Calculated at 20% utilisation
Local versus hosted APIs
Hosted modelUSD/1M outputBreak-evenAPI at your volumeVerdictComparison type
Qwen3.8 Flash
Alibaba Cloud
$0.4213.3M/mo$2API cheaperDifferent model
DeepSeek-V4.1 Flash
DeepSeek
$0.6011.4M/mo$2API cheaperDifferent model
DeepSeek-V4 Pro
DeepSeek
$1.982.8M/mo$10API cheaperDifferent model
DeepSeek V4 Pro (Together)
Together AI
$4.401.0M/mo$29Local cheaperDifferent model
GLM-5.3
Z.ai
$4.401.3M/mo$21About equalDifferent model
Claude Haiku 4.5
Anthropic
$5.001.6M/mo$18API cheaperDifferent model
Different-model comparison: the hosted model is not the model you would run locally. Treat this as a workload-quality trade-off, not a direct economic equivalence — the frontier model may complete a task in fewer tokens, or complete tasks the local model cannot. Break-even is the monthly output volume at which API spend equals the $21/month economic cost of owning this machine, assuming 8 input tokens per output token. Hosted prices are published in USD. This table is the canonical US default.