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SOURCE_REPORTED / PERFORMANCE REPORT

DeepSeek V4.1: eight-5090 placement campaign

Paired serving-optimization report on eight RTX 5090 D cards. Interactive, wide-batch and held-out text sections have different workloads.

Read the original source Publisher / project JSON ↗Markdown ↗
Source checked 2026-10-17 · Not independently reproduced by Run AI Here.

THE SOURCE IN CONTEXT

The paired experiment runs on eight 32 GB RTX 5090 D GPUs, two Xeon Gold 6530 sockets and 503 GiB host memory over PCIe. Both arms already use custom vLLM patches and CUDA graphs. The placed arm moves the same offload budget onto rarely selected expert pages and enables exact staging; it is not a stock-versus-optimized upstream comparison.

What is being compared?

The paired experiment runs on eight 32 GB RTX 5090 D GPUs, two Xeon Gold 6530 sockets and 503 GiB host memory over PCIe. Both arms already use custom vLLM patches and CUDA graphs. The placed arm moves the same offload budget onto rarely selected expert pages and enables exact staging; it is not a stock-versus-optimized upstream comparison.

Source: devin-lai · pinned document

What improves, and what regresses?

Across three trials, interactive C1 output rises from 75.37 to 97.23 tokens/s, while C8 rises from 166.55 to 247.77. The same placement reduces the random-token C1 result from 67.46 to 63.00. The negative result is central: calibration-dependent expert placement is useful when traffic resembles its routing profile and can be harmful when it does not.

Source: devin-lai · pinned document

Why is the largest number not interactive speed?

The 368.55-token/s held-out-text result is aggregate output at 128 concurrent requests, with 1,000-token inputs and 256-token outputs in a single trial. The corresponding latency and queueing behavior differ from C1. The synthetic 337.45-token/s result at 256 users is another workload and another memory budget. Do not collapse them into one headline for a single person.

Source: devin-lai · pinned document

How strong is the evidence?

The paired interactive comparison has three trials, while placement-only screens and wide-batch campaigns have one. The corpus and prompt shape differ across sections. The report measures the launch checkpoint, not the 1017 refresh, and the collection does not fill in missing prompt lengths or weight hashes from a different source.

Source: devin-lai · pinned document

Reported figures and their conditions

Selected facts reported by the original author. Each row has its own configuration and measurement boundary. Run AI Here has not reproduced these tests.

Attributed source results; no cross-source ranking
Configuration / workloadMetricReported valueEvidence
Interactive C1, patched baselineoutput throughput75.37 tokens/sreported valueOriginal row ↗
Interactive C1, expert placement + stagingoutput throughput97.23 tokens/sreported valueOriginal row ↗
Interactive C8, expert placement + stagingoutput throughput247.77 tokens/sreported valueOriginal row ↗
Random-token C1, baselineoutput throughput67.46 tokens/sreported valueOriginal row ↗
Random-token C1, placedoutput throughput63.0 tokens/sreported valueOriginal row ↗
Held-out text C128, 128-sequence presetaggregate output368.55 tokens/sreported valueOriginal row ↗
Interactive C1, patched baseline · output throughput · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency1
repetitions3
placementlayer offload
Interactive C1, expert placement + staging · output throughput · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency1
repetitions3
placementcold expert pages + exact staging
Interactive C8, expert placement + staging · output throughput · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency8
repetitions3
placementcold expert pages + exact staging
Random-token C1, baseline · output throughput · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency1
repetitions3
workloaduniform random token IDs
Random-token C1, placed · output throughput · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency1
repetitions3
workloaduniform random token IDs
placementcold expert pages + staging
Held-out text C128, 128-sequence preset · aggregate output · conditions
Conditions for this result only
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
modelDeepSeek V4.1 Flash launch checkpoint, not 1017
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
runtimealready patched graph-enabled vLLM
calibrationshipped mixed real-text profile
concurrency128
input_tokens1000
output_tokens256
repetitions1
max_num_seqs128

AI-assisted source reading and original editorial synthesis; no independent model execution.

Key reading notes

Source identity and full condition receipt

Source identity

Attribution and exact source identity
Original publisherdevin-lai
Reference typePerformance report
Model IDDeepSeek-V4.1-Flash (base checkpoint)
Default recipe artifactNot specified
Runtime topics["vLLM"]
Source revision4a4e88ec2cdcf2fc7c802e1f6b7c943d897770c8
Source commit date — not experiment date2026-09-16T07:56:48Z
Source checked2026-10-17
Declared licenseMIT

Reported conditions

Unknown fields remain unspecified; context limits and measured input lengths are different
system8 x NVIDIA RTX 5090 D (32 GB each); 503 GiB host RAM; PCIe only
node_count1
parallelismTP8 + expert parallelism
kv_dtypeNot specified
context_limit_tokensNot specified
experiment_date2026-09-16
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokensNot specified
concurrencyNot specified
cache_stateNot specified
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
speculative_decodingNot specified
temperatureNot specified
top_pNot specified

Paired interactive placement

Conditions of this profile only
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokensNot specified
concurrency[1, 8]
cache_stateNot specified
quantizationMXFP4 experts / FP8 dense / BF16 expert activations
speculative_decodingNot specified
temperatureNot specified
top_pNot specified
hardware8 x RTX 5090 D; 503 GiB RAM
repetitions3
Original experiment section

Wide-batch synthetic workload

Conditions of this profile only
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokens1024
output_tokens128
concurrencyNot specified
cache_stateNot specified
quantizationNot specified
speculative_decodingNot specified
temperatureNot specified
top_pNot specified
repetitions1
Original experiment section

Held-out text workload

Conditions of this profile only
model_revisionNot specified
weight_hashNot specified
runtime_execution_revisionNot specified
input_tokensNot specified
output_tokens256
concurrencyNot specified
cache_stateNot specified
quantizationNot specified
speculative_decodingNot specified
temperatureNot specified
top_pNot specified
repetitions1
Original experiment section

Limits

The complete original tables and raw evidence remain in the linked reports. Selected attributed figures retain their own conditions. Results from different models, weights, runtimes, workloads or cohorts must be read separately.

License scope

Repository metadata only; per-file, model, data and conversion terms remain separate.

This index publishes original explanations, limited attributed factual selections and source pointers. It does not distribute model weights or source repositories. Third-party material retains its own terms.

Related references — check the checkpoint