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Talifun Tokenizer

Lowest LatencyHighest Throughput

Get measurable performance gains across your AI workloads

Overview 01/11

Built for high-throughput AI pipelines

Talifun Tokenizer removes tokenization as a bottleneck across production AI workloads.

Fast
  • Sub-millisecond p99 latency
  • GB/s throughput
  • Scales across parallel execution
Drop-in
  • Use existing hardware
  • Keep your software architecture
  • Keep token IDs and counts exactly the same
Convenient
  • Source code access
  • Python, Node.js, and Rust packages
  • Unlimited lifetime use license
  • Low risk, easy to measure results
www.talifun.com 01/11
Performance 02/11

Throughput and p99 latency

Talifun vs next fastest tokenizer - o200k model

See all benchmarks
Throughput
Throughput uplift
P99 latency
P99 reduction
Python
832.65 MB/s
19x faster
0.34 ms
6.9x lower
Node.js
928.39 MB/s
9.5x faster
0.40 ms
6.8x lower
Rust
943.20 MB/s
9.5x faster
0.23 ms
5.6x lower
www.talifun.com 02/11
Business Impact 03/11

Modelled saving across model sizes

Pilot will capture your actual workload savings.

Use case calculator
1B model 64 x 1M token pool
70B model 64 x 1M token pool
405B model 64 x 1M token pool
Request latency saved
Pool tokens served
Request latency saved
Pool tokens served
Request latency saved
Pool tokens served
Inference / Chat Pipeline
9.20%-34.5%
10.1%-52.8%
4.27%-14.2%
4.46%-16.5%
1.41%-4.42%
1.43%-4.62%
API Gateway Token Accounting
12.7%-76.0%
14.6%-315.9%
12.7%-76.0%
14.6%-315.9%
12.7%-76.0%
14.6%-315.9%
Online RAG Query-Time
12.2%-18.5%
13.9%-22.8%
7.90%-11.8%
8.58%-13.3%
3.39%-4.97%
3.51%-5.23%
RAG Ingest / Indexing
3.51%-5.45%
3.64%-5.76%
3.51%-5.45%
3.64%-5.76%
3.51%-5.45%
3.64%-5.76%
Embedding / Reranking
4.11%-25.7%
4.29%-34.6%
0.74%-9.02%
0.74%-9.91%
0.15%-2.19%
0.15%-2.24%
Evaluation / Regression
30.6%-37.2%
44.1%-59.1%
30.6%-37.2%
44.1%-59.1%
30.6%-37.2%
44.1%-59.1%
www.talifun.com 03/11
Customer Pain 04/11

Your workloads are waiting on Tokenization

Tokenization is the step that turns human text into the numbered pieces an AI model understands. It sits directly in the critical path to your AI workloads.

Serving

  • Prompt preparation
  • Padding choices
  • Scheduling efficiency
  • Routing
  • Batch admission

RAG

  • Chunking
  • Query assembly
  • Retrieved context
  • Reranking
  • Repeated token counting
  • Token materialization

Embeddings

  • Batch creation
  • Input shaping
  • Tokenizer-paced throughput
  • Pre-vectorization bottleneck

Evals

  • Large regression suites
  • Safety suites
  • Prompt re-tokenization
  • Reference re-tokenization
  • Trace re-tokenization
  • Generated output re-tokenization

Gateways

  • Token accounting
  • Metering
  • Rate limits
  • Routing
  • Every request path
www.talifun.com 04/11
Why This Matters Now 05/11

Context windows keep getting larger

Tokenization performance costs are becoming visible in production metrics as prompts carry more of the workload into every model call.

Diagram showing modern context growth from simple prompts to large context windows filled with instructions, retrieved documents, tool outputs, code, logs, and records.
www.talifun.com 05/11
Why This Matters Now 06/11

Each exchange cumulatively grows the context

Each reply carries the conversation so far, adds new text, and sends a larger prompt through tokenization again.

Diagram showing previous context plus new state becoming the next context, which is carried into the following turn.
www.talifun.com 06/11
Why This Matters Now 07/11

Fill the inference buffer for maximum throughput

Batch multiple similar sized contexts with as little padding as possible.

Diagram showing multiple contexts being batched together to reduce repeated execution overhead and process more context per run.
www.talifun.com 07/11
Customer Outcomes 08/11

What Talifun Tokenizer can do for your workloads

Faster tokenization turns into faster experiences, more capacity, and lower operational overhead.

LatencyFaster responses

Make customer-facing AI feel quicker, with less waiting before an answer starts.

CapacityMore requests

Serve more users and traffic spikes without immediately expanding your infrastructure.

FreshnessFaster RAG ingest

Bring new documents and updates into search experiences sooner.

UtilizationBetter batch efficiency

Get more value from the hardware you already pay for during busy periods.

OverheadReduced gateway overhead

Keep customer traffic moving smoothly while still managing usage and access.

www.talifun.com 08/11
Where It Fits 09/11

Talifun fits everywhere text is converted to tokens

From chat and RAG to gateways, embeddings, training, and evals, Talifun accelerates the repeated conversion work every pipeline depends on.

Inference / Chat Pipeline

Request intakeBuild promptTokenizeRun prefill/decodeWrite response

Online RAG Query-Time

Query intakeRetrieve contextRerank/selectBuild promptTokenizeCall modelWrite answer

Agentic RAG Orchestration

Task intakeRun plannerCall tools/retrievalRebuild stateTokenizeRun reasoning stepWrite final response

API Gateway Token Accounting

Request intakeEvaluate policyTokenizeDecide admissionUpdate quota/billingRoute request

Moderation / Classification

Message intakeTokenizeRun classifierDecide policyGate downstream

Embedding / Reranking

Load itemsTokenizePack batchRun embed/rerankWrite vectors/scores

RAG Ingest / Indexing

Load documentsParse/extractChunk textTokenizeEmbed chunksWrite index

Offline Training Corpus Build

Load corpusFilter/dedupeTokenizePack sequencesWrite shardsFinalize dataset

Online Training Input Pipeline

Loader readTokenizePrefetchH2D transferRun GPU stepHandoff batch

Evaluation / Regression

Load prompt suiteExpand variantsTokenizeRun evalsCompute metricsDecide release
www.talifun.com 09/11
Partnership 10/11

Founder-led partnership, with industrial-grade execution

Customers get direct access to the builder, fast technical judgment, and accountable ownership on this specialist topic.

Founder-led support
  • Direct access to the builder
  • Issues
  • Traces
  • Correctness questions
  • Rollout decisions
Stability & Systems
  • Documented integration path
  • Correctness testing
  • Pinned packages
  • Rollback planning
  • Trace-backed acceptance criteria
Traction-led Proof
  • Buying case based on measured impact
  • Real traces
  • Benchmark deltas
  • CPU profile
  • Rollout risk
www.talifun.com 10/11
Pilot Offer 11/11

Run a paid focused pilot

We bring the tokenizer, benchmark support, and, where permitted, hands-on help making the integration changes. You bring representative workloads, the target model configuration, and the current tokenizer behavior to compare against.

OfferTrace-backed paid pilot

Use real prompts, RAG paths, gateway accounting, batch jobs, or eval workloads to prove where faster tokenization changes the customer outcome. If permitted, we help make the integration changes.

What we needA measured path to replay

Representative traces, model configuration, expected token IDs and counts test cases, and the latency, throughput, or CPU metrics that matter.

DecisionEvidence-backed recommendation

Measured impact, correctness evidence, and a clear go/no-go decision for production adoption.

01Configure tokenizer for model
02Replace tokenizer call
03Verify outputs
04Review metrics
05Decision
www.talifun.com 11/11