gpt-oss-20b on CPU-only workstation (Ryzen 9950X, 192 GB DDR5)
20.915B (3.6B active) on 192 GB at 90 GB/s. Hardware details · Model details
CompatibilityComfortable
Fits
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
MXFP4
Calculated gguf
Memory required
13.0 GB
Calculated of 176.6 GB usable — 7%
Max practical context
128K
Calculated model supports 128K
Memory budget at 8K context
Model weights ⓘ
11.3 GB Calculated
KV cache ⓘ
0.4 GB Calculated
Runtime overhead ⓘ
1.3 GB Estimated
Total required
13.0 GB Calculated
Usable memory ⓘ
176.6 GB Assumption
Headroom
163.6 GB Calculated
Highest-precision quantization that leaves headroom: uses 7% 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 20.915B parameters must be resident in memory even though only ~3.6B are active per token. Memory follows total parameters; speed follows active parameters.
PerformanceEstimated0/10
Decode (generation)
~16.1–23.1 t/s
Estimated calculated, not measured
Prefill (prompt)
~47.9–99.6 t/s
Estimated
TTFT at 8K
~82.3–171.1 s
Estimated time to first token
Power while generating
190 W
Measured 0.30 tokens/s per watt
These figures are estimated, not measured. Nobody has published a benchmark of gpt-oss-20b on this machine that we know of, so we calculate throughput from memory bandwidth and active parameter count. Estimates are always shown as a range. Expand the working below to see exactly how the number was produced.
How the estimate is calculated
- Decode: reading 3.6B active parameters at 4.25 bits/weight takes 42.69 ms at 90 GB/s x 50% achieved efficiency.
- MoE routing penalty of 15% applied: expert gathers are less bandwidth-efficient than a dense sweep.
- Prefill: 4 TFLOPS (FP16) x 32% assumed model-FLOPs utilisation, divided by 2 x 8.7B parameters per token.
- Prefill uses 8.7B effective parameters, not the 3.6B 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.
This model at other quantizations on this machine
| Quantization | Weights | Total needed | Utilisation | Fit | Max context |
|---|---|---|---|---|---|
MXFP4recommended | 11.3 GB | 13.0 GB | 7% | Comfortable | 128K |
BF16 | 39.0 GB | 41.5 GB | 23% | Comfortable | 128K |
Cost of running gpt-oss-20b 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
$8.27
Calculated 2.5M 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.
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
- VAT rate: No US federal VAT · verified 2026-09-07
- Marginal tax rate: IRS Publication 542 — Corporations · verified 2026-09-07
- VAT recoverable fraction: site assumption · verified 2026-09-07
- Useful life: IRS Publication 946 — How To Depreciate Property · verified 2026-09-07
- Electricity price: Site assumption · verified 2026-09-07
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
12.8 t/s
Calculated purchase price only
Decode per $100/month
95.4 t/s
Calculated after-tax ownership cost
Tokens per joule
0.30
Calculated same as tokens/s per watt
USD per 1M output tokens
$8.27
Calculated at 20% utilisation
Local versus hosted APIs
| Hosted model | USD/1M output | Break-even | API at your volume | Verdict | Comparison type |
|---|---|---|---|---|---|
| Qwen3.8 Flash Alibaba Cloud | $0.42 | 13.3M/mo | $4 | API cheaper | Different model |
| DeepSeek-V4 Flash DeepSeek | $0.66 | 8.5M/mo | $6 | API cheaper | Different model |
| DeepSeek-V4 Pro DeepSeek | $1.98 | 2.8M/mo | $18 | API cheaper | Different model |
| DeepSeek V4 Pro (Together) Together AI | $4.40 | 1.0M/mo | $53 | Local cheaper | Different model |
| GLM-5.3 Z.ai | $4.40 | 1.3M/mo | $39 | Local cheaper | Different model |
| Claude Haiku 4.5 Anthropic | $5.00 | 1.6M/mo | $32 | Local cheaper | Different 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.
gpt-oss-20b on other hardware
| Hardware | Decode | Price | Fit |
|---|---|---|---|
| Quad RTX 5090 workstation (4x 32 GB) | ~387–556 t/s | €13.999 | Comfortable |
| RTX 5090 workstation (1x 32 GB) | 419 t/s | €3.699 | Comfortable |
| RTX PRO 6000 Blackwell workstation (96 GB) | ~329–474 t/s | €11.499 | Comfortable |
| RTX PRO 6000 Max-Q workstation (96 GB, 300 W) | ~329–474 t/s | €11.999 | Comfortable |
| Dual RTX 5090 workstation (2x 32 GB) | ~324–466 t/s | €6.499 | Comfortable |
| Mac Studio M5 Ultra 512 GB | ~261–376 t/s | €12.599 | Comfortable |
Nearest alternatives to the CPU-only workstation (Ryzen 9950X, 192 GB DDR5)
gpt-oss-20b on Quad RTX 5090 workstation (4x 32 GB)128 GBgpt-oss-20b on Used RTX 4090 workstation (24 GB)24 GBgpt-oss-20b on Radeon AI PRO R9700 workstation (32 GB)32 GBgpt-oss-20b on RTX PRO 6000 Blackwell workstation (96 GB)96 GBgpt-oss-20b on RTX PRO 6000 Max-Q workstation (96 GB, 300 W)96 GB
Compare CPU-only workstation (Ryzen 9950X, 192 GB DDR5) against Quad RTX 5090 workstation (4x 32 GB) →
Compare CPU-only workstation (Ryzen 9950X, 192 GB DDR5) against Quad RTX 5090 workstation (4x 32 GB) →
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