How to Tune Memory Usage for OpenFlux on a Small VPS: 7 Proven Techniques

OpenFlux runs a user-space TCP stack (gVisor) that allocates large buffers per connection, but you can reduce RAM consumption to below 256 MiB by shrinking TCP buffers, disabling debug logging, switching to the Yandex transport, and limiting concurrent connections.

OpenFlux embeds a complete gVisor TCP/IP implementation inside the exit-node process, providing isolation at the cost of per-connection memory overhead. On small VPS instances with 512 MiB or less RAM, default buffer allocations and verbose logging can quickly exhaust available memory. This guide explains how to tune memory usage for OpenFlux on a small VPS by adjusting parameters in tunnel/tunnel.go, main.go, and transport implementations to achieve stable operation with minimal resources.

Understand OpenFlux Memory Architecture

OpenFlux manages TCP connections through a userspace networking stack rather than the host kernel. Each active connection instantiates a gVisor "stack" object that maintains independent send and receive buffers, packet structures, and goroutines. The primary memory consumers include:

• gVisor TCP buffers – Default allocations of approximately 2 MiB per direction per connection in tunnel/tunnel.go
• Log ring buffers – In-memory slice storage for debug output controlled by export_ios.go and utils/logging.go
• ICE candidate caching – Unbounded buffering in transport/oneme/max_call.go (line 281) when using the MAX transport
• Goroutine stacks – Approximately 2 KB per goroutine for each TCP stream's read/write operations
• Compression buffers – Temporary slices allocated during packet processing in compressor.go

Reduce TCP Buffer Sizes in tunnel.go

The most significant memory savings come from reducing the gVisor TCP buffer range. In tunnel/tunnel.go at line 30, the SetTCPBuffers function applies default ranges of roughly 2 MiB per direction. For memory-constrained VPS deployments, reduce these values using the --recv-buffer and --send-buffer flags parsed in main.go:

./universal-bypass-tool --exit-node \
  --recv-buffer=65536 \
  --send-buffer=131072 \
  --url "YOUR_DOC_URL"

Setting receive buffers to 64 KB and send buffers to 128 KB reduces per-connection RAM usage from 4 MiB to 192 KB, allowing hundreds of connections on small instances.

Disable Debug Logging and Ring Buffers

When --debug is enabled, OpenFlux stores every log line in an in-memory ring buffer defined in export_ios.go and utils/logging.go. Under high traffic, this buffer accumulates millions of entries, consuming tens of megabytes. Disable debug mode to eliminate this overhead:

./universal-bypass-tool --exit-node --debug=false --url "YOUR_DOC_URL"

If you require minimal logging, modify the ring buffer size constants in utils/logging.go rather than using the debug flag.

Switch to the Yandex Transport

The MAX transport defined in transport/oneme/max_call.go maintains an h.pendingCandidates slice (line 281) that buffers ICE candidates during WebRTC negotiation. Under poor network conditions, this buffer grows unbounded and can dominate memory usage. The Yandex transport implements the same Transport interface in transport/transport.go without ICE candidate buffering, using a significantly smaller code path:

./universal-bypass-tool --exit-node --transport yandex --url "YOUR_DOC_URL"

For small VPS deployments, the Yandex transport typically reduces memory footprint by 40-60% compared to MAX.

Limit Concurrent Connections and Goroutines

Each TCP stream spawns dedicated goroutines for reading and writing, consuming approximately 2 KB of stack space plus heap allocations. While OpenFlux does not expose a --max-clients flag, you can enforce connection limits using iptables to prevent goroutine exhaustion:

sudo iptables -A INPUT -p tcp --dport 1080 -m connlimit --connlimit-above 20 -j REJECT

This example rejects new connections after 20 simultaneous streams, keeping total goroutine memory overhead under 100 KB for the networking layer.

Optimize Compression and Go Runtime Settings

The compressor.go file allocates intermediate buffers for packet compression. Disable compression entirely or select less memory-intensive algorithms to avoid temporary slice allocations. Additionally, constrain the Go scheduler on single-core VPS instances by setting GOMAXPROCS:

GOMAXPROCS=1 ./universal-bypass-tool --exit-node --url "YOUR_DOC_URL"

This reduces OS thread overhead and prevents the Go runtime from reserving memory for idle processors.

Complete Low-Memory Deployment Example

Combine these optimizations to run OpenFlux comfortably on a 256 MiB VPS:

GOMAXPROCS=1 ./universal-bypass-tool \
  --exit-node \
  --url "https://doc.example.com" \
  --transport yandex \
  --recv-buffer=65536 \
  --send-buffer=131072 \
  --debug=false

Pair this configuration with the iptables connection limiter to prevent memory exhaustion during traffic spikes.

Summary

• Shrink TCP buffers using --recv-buffer and --send-buffer to reduce per-connection allocation from 4 MiB to under 200 KB as implemented in tunnel/tunnel.go
• Disable debug logging to eliminate the in-memory ring buffer that accumulates entries in export_ios.go and utils/logging.go
• Use the Yandex transport instead of MAX to avoid unbounded ICE candidate buffering in transport/oneme/max_call.go
• Limit concurrent connections with iptables rules to control goroutine count and stack usage
• Set GOMAXPROCS=1 on single-core VPS instances to minimize runtime memory overhead
• Disable compression in compressor.go if memory is prioritized over bandwidth

Frequently Asked Questions

What is the minimum RAM required to run OpenFlux on a small VPS?

According to the source code analysis, you can run OpenFlux on a VPS with as little as 256 MiB of RAM by applying the optimizations described above—specifically reducing TCP buffers to 64-128 KB, disabling debug logging, and using the Yandex transport. Without tuning, the default 2 MiB buffers per direction require at least 1-2 GiB for moderate connection counts.

Why does OpenFlux consume more memory than standard SOCKS proxies?

OpenFlux uses gVisor's user-space TCP stack (implemented in tunnel/tunnel.go) rather than the host kernel's networking stack. This provides better isolation and consistency across platforms, but requires allocating separate send/receive buffers for every TCP connection in userspace, whereas kernel TCP reuse would share buffer pools. This architectural choice explains the higher base memory cost per connection.

How can I monitor OpenFlux memory usage in real-time?

When compiled with debug support, OpenFlux prints memory usage summaries to stderr when --debug is enabled, as implemented in utils/logging.go. For production monitoring, use standard Linux tools like top or htop to track the resident set size (RSS) of the universal-bypass-tool process, or inspect the h.pendingCandidates slice length in transport/oneme/max_call.go if using the MAX transport to detect candidate accumulation.

Is the Yandex transport less secure than the MAX transport?

No, both transports implement the same Transport interface defined in transport/transport.go and provide equivalent encryption and authentication. The Yandex transport simply omits the WebRTC ICE candidate buffering logic found in max_call.go, making it more memory-efficient without compromising security. The MAX transport's additional complexity serves NAT traversal scenarios, not security enhancement.

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