# How Cluster Mode with vLLM Tensor Parallelism Distributes Work Across DGX Spark Nodes in LLMFIT

> Learn how LLMFIT uses vLLM tensor parallelism in cluster mode to distribute inference workloads across DGX Spark nodes. Discover how model layers are partitioned for efficient distributed computing.

- Repository: [Alex Jones/llmfit](https://github.com/AlexsJones/llmfit)
- Tags: deep-dive
- Published: 2026-08-21

---

**LLMFIT distributes inference workloads across NVIDIA DGX Spark nodes using `RunMode::TensorParallel`, which partitions model layers at the tensor level while NCCL handles the underlying inter-node communication and memory aggregation.**

LLMFIT (AlexsJones/llmfit) implements cluster-mode tensor parallelism to scale large language model inference across multi-node GPU clusters. When deployed on NVIDIA DGX Spark infrastructure, the system treats the aggregate cluster VRAM as a unified memory pool and delegates data distribution to NCCL, enabling vLLM-compatible tensor parallelism without manual sharding configuration.

## Understanding the TensorParallel Run Mode Architecture

The core distribution logic resides in the runtime mode selection and memory analysis engine.

### The RunMode Enum and NCCL Distribution

In [`llmfit-core/src/fit.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-core/src/fit.rs), the `RunMode` enum defines the **TensorParallel** variant specifically for multi-node deployments:

```rust
// Lines 189-195 in llmfit-core/src/fit.rs
pub enum RunMode {
    Single,
    /// Distributed via NCCL across cluster nodes
    TensorParallel,
    PipelineParallel,
}

```

When `RunMode::TensorParallel` is selected, LLMFIT assumes the model weights will be sharded across GPUs using **NCCL** (NVIDIA Collective Communications Library) primitives. The runtime does not implement custom networking logic; instead, it annotates that NCCL will handle the distribution of tensors during forward passes, enabling the vLLM-style tensor parallelism that DGX Spark clusters are optimized for.

## Cluster-Wide Memory Accounting

The distribution mechanism relies on accurate aggregate resource calculation across the entire cluster.

### Aggregate VRAM Calculation

According to the source analysis in [`llmfit-core/src/fit.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-core/src/fit.rs), lines 401-402, the memory estimation logic calculates total available VRAM as the sum across all detected nodes:

```rust
// Memory accounting logic in fit.rs (lines 401-402)
// Total VRAM is the sum across all nodes (NCCL handles distribution)
let total_vram: f64 = nodes.iter().map(|n| n.vram_gb).sum();

```

This approach reflects the reality of tensor parallelism: **each DGX Spark node holds only its partition of the model weights**, so the system must sum individual node capacities to determine if the cluster can accommodate the full model. The per-node memory requirement is approximately `total_model_vram / number_of_nodes`, with NCCL managing the synchronization overhead.

### Communication Layer

During inference, NCCL executes all-reduce and broadcast operations across the DGX Spark fabric to reconcile partial results from each node's tensor slice. LLMFIT abstracts this complexity by marking the run mode as `TensorParallel` and allowing the underlying vLLM-compatible runtime to manage the actual NCCL calls.

## User Interface and API Exposure

LLMFIT exposes the tensor parallelism status through multiple interfaces to ensure operators can verify cluster mode is active.

### CLI and TUI Indicators

In [`llmfit-tui/src/display.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/display.rs) (lines 732-736) and [`llmfit-tui/src/tui_ui.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/tui_ui.rs), the system renders tensor parallelism status as **"TP"** in tables and terminal interfaces:

```rust
// Display rendering in display.rs
match run_mode {
    RunMode::TensorParallel => "TP".to_string(),
    _ => "S".to_string(),
}

```

### HTTP API Integration

The shared server logic in [`llmfit-tui/src/serve_shared.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/serve_shared.rs) (line 96) exposes the mode string for REST API consumers:

```rust
// API serialization in serve_shared.rs
"run_mode": "tensor_parallel"

```

This allows external orchestrators to verify that DGX Spark nodes are operating in distributed mode rather than single-node inference.

## Practical Implementation Examples

Enable cluster mode with vLLM tensor parallelism using the following interfaces:

### Command Line Interface

```bash

# Run a model using Tensor Parallelism on a DGX Spark cluster

llmfit fit \
  --model llama-2-70b \
  --run-mode tensor_parallel \
  --max-context 4096

```

### Programmatic Rust API

```rust
// Selecting TensorParallel programmatically
use llmfit_core::fit::{RunMode, ModelFit};

let fit = ModelFit::analyze_with_forced_runtime(
    &model,
    &system_specs,
    None,                     // no explicit context limit
    None,                     // keep automatic runtime selection
);
assert_eq!(fit.run_mode, RunMode::TensorParallel);

```

### HTTP API Query

```bash

# Query cluster status for TensorParallel results

GET /api/v1/fit?model=llama-2-70b&run_mode=tensor_parallel

```

## Summary

- **`RunMode::TensorParallel`** in [`llmfit-core/src/fit.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-core/src/fit.rs) (lines 189-195) triggers cluster-mode distribution across DGX Spark nodes
- **NCCL** handles the actual tensor distribution and communication between nodes, not LLMFIT itself
- **Memory calculation** sums VRAM across all nodes (lines 401-402) because each node holds only a partition of the model weights
- **UI components** in [`display.rs`](https://github.com/AlexsJones/llmfit/blob/main/display.rs) and [`tui_ui.rs`](https://github.com/AlexsJones/llmfit/blob/main/tui_ui.rs) render the mode as "TP" for quick identification
- **API exposure** in [`serve_shared.rs`](https://github.com/AlexsJones/llmfit/blob/main/serve_shared.rs) (line 96) identifies the mode as `"tensor_parallel"` for downstream orchestration

## Frequently Asked Questions

### How does LLMFIT calculate memory requirements for cluster mode?

LLMFIT calculates total available memory by summing VRAM across all detected DGX Spark nodes, as implemented in [`llmfit-core/src/fit.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-core/src/fit.rs) at lines 401-402. Each node loads only a slice of the model weights, so the system verifies that the aggregate cluster memory exceeds the model size before allowing tensor parallelism to commence.

### What role does NCCL play in LLMFIT's tensor parallelism?

NCCL (NVIDIA Collective Communications Library) provides the low-level communication primitives that move tensor data between DGX nodes. LLMFIT marks the execution path as `RunMode::TensorParallel` and relies on the underlying vLLM runtime to invoke NCCL for all-reduce operations during inference, rather than implementing custom network protocols.

### How do I enable tensor parallelism on my DGX Spark cluster?

Specify `--run-mode tensor_parallel` when invoking the `llmfit fit` command, or use the HTTP API with the `run_mode=tensor_parallel` parameter. The system automatically detects all available GPUs across the cluster and calculates the appropriate sharding strategy based on the aggregate VRAM reported in the analysis phase.

### Which files control the TensorParallel mode UI indicators?

The **"TP"** abbreviation appears in [`llmfit-tui/src/display.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/display.rs) (lines 732-736) for CLI table rendering and in [`llmfit-tui/src/tui_ui.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/tui_ui.rs) for the terminal interface. The string representation `"tensor_parallel"` for API responses is defined in [`llmfit-tui/src/serve_shared.rs`](https://github.com/AlexsJones/llmfit/blob/main/llmfit-tui/src/serve_shared.rs) at line 96.